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Part 8 - Examples of h&m delivery, Good and Bad Writing. Learning to write often works best by example. The following are excerpts from nine first-year student essays. Most of the examples are bad, although I did find a two good examples in the bunch. In most cases, the tns earthing, names and dates from the essays have been changed to not compromise the subject matter for future students (in other words, don't use any of the apparent research information here in code your papers). I have tried to Cloning Trials Essay, categorize the errors as best as I could. Errors or bad portions are usually bolded to help you identify them. Smith was a religious, Christian man. His notion of monads included contextual references to h&m delivery code, God. He believed that God controls the harmony of life through these monads.

The essay then goes on to discuss these monads in a Christian context. Had the student omitted the above sentences, however, the list of short, discussion of religion would have been completely out of place, given the essay's topic. But since the code, person being discussed had religious views that affected his theories and work, it is relevant to mention the religious aspect. Had Smith's religion not been a direct influence on his work, it would have been irrelevant. Similarly, you wouldn't mention other things about someone in an essay if it wasn't relevant to the topic. For example, it is irrelevant to list term for a student, mention a scientist's race in an essay about their discovery unless the race impacted the discovery. An example of this might be if a black scientist's prime motivation to find a cure for h&m delivery sickle cell anemia was because that disease strikes black people in proportionally higher numbers. If the same scientist was researching some aspect of physics, it would probably not be relevant to mention the race at all. An introductory paragraph: On March 4, 1849, John Smith was born to Anna Bradcock Smith and James Smith. Although certainly not of humble origins, John was acquainted with several prominent and influential men of politics with whom he discussed matters of mathematics, history, science, logic, law, and theology.

Smith was brilliant in each of islam rituals, these fields, but he became known particularly for h&m delivery code his contributions in market the fields of philosophy, mathematics, and logistics. This paper will not only shed light on some of Smith's theories and words regarding these three areas, but will also tell of the events in his life that made him the man that he was. This is the introduction to a chronologically-ordered essay about Smith's life and discoveries. As such, the choice to begin with his date of birth is a good one. The paragraph summarizes the fields touched by Smith and also mentions the h&m delivery, key areas he studied. The paper sets up an expectation for the reader of both a detailed explanation of Smith's discoveries and anecdotes describing his personality. The sentence structure is grammatically sound and flows well.

In the late 1650's , Smith's mother returned to London , she then pulled him out of school with the intent to make him a farmer . Apostrophes indicate possessiveness or contractions, not plurality. The decade is the 1650s. The sentence is a run-on. Punishment Essay. It should either end after London, beginning a new sentence with She then, or the she then should be changed to code, and. To make someone a farmer is to create a farmer for Capital Is Barbaric them. The student meant: to turn him into a farmer or to encourage him to be a farmer. Smith invented the h&m delivery code, widgetiscope and paved the way for Cloning Essay future widget watching. All-the-while remaining a simple and humble man who considered himself to be part of a team working for the greater good. The bolded part is not a complete sentence. The entire thing should be one sentence. All-the-while does not require hyphenation.

The two differing approaches of development already described, eventually led to the development of the two original branches of widgetry; fingleish and fnordleish. This sentence is mispunctuated. The comma is confusing and should be removed, and the semicolon should be a colon. Another of Smith's ideas was the method of differentiation. H&m Delivery Code. The university re-opened after the plague in 1667. Smith was elected to a minor fellowship, and awarded a major fellowship after he received his Master's Degree (Bogus 4). After the realization that Calculus was important, and about of Versailles, was being recognized, a document to record all of the theories became a necessity. The Methodis Differantium, the h&m delivery, document that contained the elements of the theory of differentiation, was created in 1667. Smith believed he was being pulled in two directions when it came to publishing his theories and Essay and the of Treaty, making his work known.

He felt a need for h&m delivery code fame and fortune, yet on Cloning Trials, the other hand he had an h&m delivery, abundant fear of rejection. To the dismay of many future mathematicians, it was never published because of Smith's fear of criticism. Since he was not focusing on publishing his work, Smith pursued his career as a professor. This so-called paragraph is an utter mess. There are far too many ideas in it, all of which are strung together haphazardly without any logical flow. Tns Earthing. I'll try to dissect and rewrite it, but I won't make errors bold because the entire paragraph would be bold if I did. First, let's pick out the h&m delivery, different topics being addressed: the method of differentiation the university re-opening after the plague Smith's ascension through the what market system, university ranks the need of a document detailing differentiation, which was eventually created Smith's mental state, desires and fears.

Now, if we replace each sentence with the number of the h&m delivery, corresponding idea, we can see what a jumbled mess this is: 1, 2, 3, 4, 4, 5, 5, 4, 3. Don't introduce a paragraph with one topic and then leap to islam rituals, another topic in code the next sentence. While it may sometimes be necessary to mention something as an Capital Punishment Is Barbaric, aside to complement the topic, the return to the topic should be swift and easy to understand. Don't bounce around within the paragraph as this student has done. Another problem: there doesn't seem to be a coherent timeline within the paragraph. H&m Delivery. Did the university re-open in 1667, or was the plague in 1667? Is the student saying that Smith was elected to a minor fellowship that year or another year? Similarly, when did the major fellowship and Master's Degree come in? It's unlikely to have all happened in one year, though it is possible. The document was created in 1667, it seems, but when did Smith decide not to publish and seek work as a professor instead?

Also 1667? It sounds like that was a very busy year for poor Smith! The sentences themselves are also awkwardly constructed, making the is a market, entire thing hard to understand. I'll make some assumptions regarding the confusing date information. Here is h&m delivery code, how this information should have been presented: Smith's ideas on the method of differentiation were gaining recognition in tns earthing the mathematical community, which made it necessary for him to produce a document detailing all of his theories on the subject. H&m Delivery. Thus, when the university re-opened in 1667 following the plague and Smith was elected to a minor fellowship, he wrote Methodis Differantium. Although Smith wished to system, attain fame and fortune, he also feared rejection.

This dichotomy resulted in his failure to publish Methodis Differantium; a failure that would be mourned by mathematicians well into the future. Still, Smith was awarded a major fellowship after receiving his Master's Degree in [insert year]. Since he was not interested in publishing his work, he concentrated instead on pursuing a position as a professor. Queen Esmerelda knighted Jones in 1705 to be given the title of h&m delivery code, Sir Joe Smith, which made him the islam rituals, first scientist to be so honored for his work (Bogus) . The phrase to be given is awkward here. It would be better written: Queen Esmerelda knighted Jones in 1705, which gave him the title of Sir. Who else could be honoured for Smith's work other than Smith? It should say: . which made him the first man to be honored for scientific work.

There probably should be a page number listed in the citation. Jones had a main idea of analytic geometry. What does this mean? Does the h&m delivery, student mean that one of Jones' main ideas concerned analytic geometry? Does he mean that one of the main ideas of analytic geometry was conceived by Jones? Or does he mean something else entirely? This makes little sense and is very awkward. Whether Smith made no use of the manuscript from which he had copied abstracts , or whether he had previously invented the widgetiscope, are questions on which at Is Barbaric Essay, this distance of time no direct evidence is available . If Smith made no use of the code, manuscript, he can't have used it to copy abstracts. This is list of short term student, a very awkward way of saying that the h&m delivery code, events in question happened so long ago that there is no longer sufficient evidence to answer certain questions. It would be better written:

Questions as to whether Smith made further use of the manuscript from which he copied abstracts or whether he had previously invented the widgetiscope are rooted so far in the past that it is impossible to gather sufficient direct evidence to provide answers. This is Capital Punishment, still a bit awkward. It's best when broken up into smaller sentences: There are still questions as to whether Smith made further use of the h&m delivery, manuscript from Human Trials, which he copied abstracts or whether he had previously invented the code, widgetiscope. Such questions are rooted so far in the past, however, that it is impossible to gather sufficient direct evidence to provide answers. Smith formed a political plan to try to persuade the Germans to Punishment Is Barbaric Essay, attack the French due to him not agreeing with their political agendas and this proved the means of his visiting Hamburg. Due to him not agreeing with is a very awkward way of saying: because he disagreed with. The second bolded part should be a separate sentence. Proved the means of his visiting is a very awkward way of h&m delivery code, saying is why he visited. Jones explained ideas too enormous to understand, and simplified problems too complex to approach. Not only is this hyperbole, it's also logically impossible.

If the ideas were too complicated to understand, Jones couldn't have understood them himself. If the problems were too complex to approach, Jones could not have approached them. Mismatched Words, Phrases, and tns earthing, Pronouns. After marrying Elizabeth, Smith's father fell ill for several months. After no sign of h&m delivery code, recovery, a lawyer was summoned to the manor. A will was drawn up, including one hundred acres of Essay about of Treaty of Versailles, land, the manor house, livestock, grain, and Smith Senior's death (Bogus 10). His mother gave birth to Smith three months after Smith senior died. He was premature after suffering from illness due to the shock of her husband's passing during the fall . The phrase after no sign of recovery is not properly attached to Smith's father.

Instead, it is saying that the lawyer did not recover from something. A will does not include land, a house, etc. H&m Delivery Code. It states to whom such things are bequeathed. This should say: A will was drawn up leaving one hundred acres of land, the manor house, livestock and grain to list term, [whomever]. I don't even understand how and Smith Senior's death fits into h&m delivery this sentence. His in the sentence His mother gave birth. refers to the antecedent Smith Senior. List Of Short Term For A. Thus, Smith Senior's mother gave birth to Smith Senior's son. That would necessitate incest, and h&m delivery code, is clearly not what the student meant to list of short for a, say. They should have simply said Elizabeth gave birth.

Who else but someone's mother gives birth to them anyway? Given the confusions regarding the various Smiths, it would have been better if the student had used first names during this part of the essay. There is inconsistency in capitalization. It is Smith Senior once, and Smith senior another time. The he in he was premature again refers to h&m delivery code, the wrong antecedent. Smith Senior was not premature. Smith did not suffer illness due to Punishment, the shock of code, Smith Senior's passing.

Elizabeth did. This sentence says that Smith suffered the illness. The student suddenly introduces the phrase during the fall when no other mention of the season has been made. This could be confused with Smith Senior dying from a fall. Lastly, the inverse relationship between area and the tangent were never attained. The relationship is singular, even though it refers to multiple elements. Thus, the verb were should be singular as well, and of short term goals for a student, changed to was. It was this century where many of the worlds most honorable and highly respected mathematicians created what we know today as calculus. A century is not a place, it is a section of time. Say it is h&m delivery code, a place where. or a time when.

In this case, It was this century when. Adding an 's' without an apostrophe in what this case is pluralization, not indicative of possession. The student means world's. But perhaps the largest obstacle , which the code, Greeks could not overcome, were their insufficient number and measuring system . Were is plural, but obstacle and system are singular. It should be was. Tragically at the age of six, Smith's father died. This says that Smith's father died at tns earthing, the age of h&m delivery code, six.

The student means: Tragically, when Smith was six years old his father died. Jones, now familiar with Smith's discoveries, wrote Smith a letter soon after the publication of his discoveries. After the islam rituals, publication of h&m delivery code, whose discoveries: Jones' or Smith's? Jones reasoned that if he could calculate the angles of the projected colour, a new law of refraction could be made . People can make legal laws, but natural or scientific laws are discovered. To make a new law of refraction, Jones would have to alter physics. During the seventeenth century, the inhabitants of England did not realize the importance of scientific advancement. Inhabitants could well mean non-human creatures, and term, is thus a poor choice of h&m delivery code, a word. Are we to islam rituals, understand that ALL of the people in h&m delivery England failed to realize the importance of scientific advancement for term student an entire century? It would have been better if the student had said most people in h&m delivery code England. At the current time, the Cloning Trials Essay, dominant belief was that light traveled in h&m delivery wave . The current time is the moment the reader is reading the sentence. The student meant to say that the belief was such during the historical time period being discussed.

Current should be omitted. The phrase in wave has an market system, error. It should either be in waves or in a wave. Both may be correct, but such an error can be misunderstood if one is incorrect. This would likely have been caught if the student had read the paper out loud. Secondly, Jones' reliance on geometric algebra rather than symbolic notation created considerable impedance to the identification of solutions of computational features found frequently to different problems.

Here is an example of a student not knowing the proper meaning of a word. Code. Impedance means opposition to the flow of electric current. It does not mean the same as to impede, which is to be an obstacle. This could be an instance where a student used the thesaurus in Trials a word processor to come up with a word without bothering to check if the code, word fit the context. Hitler And The Of Versailles. It could also simply be that the student had mislearned the word themselves. Incidentally, a quick check of MS Word 97 shows synonyms to impedance to be obstruction, block, baffle, hindrance, breakwater, fin, and maze. So here is direct proof that you shouldn't always trust what a word processor thesaurus tells you is an equivalent word. Be diligent and h&m delivery code, look up unfamiliar words in the dictionary before using them in your essay.

In studying widgetry, it serves as great importance that one is aware of the two systems of widgetry; fingleish and Cloning Trials, fnordleish. Something does not serve as great importance, and one being aware doesn't fit either. This is a student trying to sound fancy but instead making no sense. The sentence should read: In studying widgetry, one should be aware of the two systems of widgetry; fingleish and fnordleish. It was thought that Jones hated his stepfather and his mother, partly for abandoning him at h&m delivery, such a young age. Who thought so?

This entire statement, which implies something that cannot be proven and is thus not a basic fact, had no attribution in the essay. Capital Essay. Since it was about someone historical and the student couldn't possibly have known this unless they got it from a source, it was plagiarism to include it without attribution. Smith managed one friendship through this time and the value of code, that is always questioned. Who is questioning the value? There is no attribution to explain who questions it or to prove that it is list term goals for a, questioned by anyone other than the h&m delivery, student. What precisely is being questioned?

The value of only having one friend, or the value of the goals student, one friendship to Smith in code particular? . which means that the cut in the # of points is what is a system, equal to the degree of the curve. Using the # symbol instead of the word number is a bad short cut, and h&m delivery, certainly inappropriate for list term for a a formal essay. Smith also helped to h&m delivery code, improve the scientific community ; his focus was mainly regarding widgetry. How does a focus on a subject help to improve a community? It might improve the understanding of the subject in Essay Hitler of Versailles the community, but does that improve the code, community itself? This is a badly worded assertion. If it truly did benefit the scientific community as a whole, the student should cite a source demonstrating that to be the list of short term goals for a, case. No attribution was present. In one day, John's attitude towards school changed for the better.

A boy ranked just above him kicked him in the stomach. At the end of the day John challenged the boy to a fight. Even though John was much smaller than his opponent, his determination overtook the boy. Winning the h&m delivery code, fight was still not enough. Trials Essay. John applied himself in class, and soon became the top student in the school. This entire paragraph introduces an anecdote for the purpose of explaining what drove John to become a better student. H&m Delivery Code. Incredibly, it manages to completely fail to mention the relationship between the anecdote and John's new-found classroom enthusiasm. The relationship is implied and the reader can guess that John wished to beat the boy in more than just a physical fight, and thus worked hard to outrank the boy in the classroom, but that is not stated. Tns Earthing. The paragraph is very choppy and the sentences do not flow well. Read it out h&m delivery, loud, and you'll hear how it sounds like a grade school book instead of a university essay.

During this time, Smith constructed a water clock. He constructed the clock out of an old box. This is Human Trials, choppy. H&m Delivery. It could be easily combined into Human Cloning one sentence. Jones became began to study motion. This error was probably due to a sentence that once legitimately contained the word became being edited without became being removed. If the student had read the h&m delivery code, essay out loud or given it to a friend to read, this error likely would have been noticed.

Yet, in 1679, Jones would discover that his initial calculation the tns earthing, Moon's distance from Earth was incorrect. Here is another example of a simple error of omission that could have been caught if the student had read the essay aloud or given it to a friend to read. The word of should be between calculation and the. That one small error makes the entire sentence awkward and confusing. If the instructor has to reread the sentence to try to understand its meaning, the h&m delivery code, flow of the essay is interrupted. If this happens often enough in the essay, it gives an overall bad impression on what otherwise might be a very good paper in terms of research.

More examples of errors that could have been caught if the students had bothered to read their essay: One of tns earthing, Smith's main contribution was his use of. Widgetry emphasized the notion of the infinite widget, which in fact cam as a great service to code, Smith in that it served as an important too in helping explain his branch of widgetry. Jones might have in fact perputuated the ideas, but he was also at a loss when he could not make good sense of them from the beginning. Admiration for Smith grew in the filed of widgetry. With Jones' encouragement, Smith drafter a number of Capital Punishment Essay, monographs on religious topics. Smith considers out h&m delivery code, universe to be a gravitational system.

On August 10, 1777, Jones was ent a letter from. In later research , it was proven that Jones was incorrect and science rejected his theories about Essay about of Treaty light until the next century. Thus, it was scientifically proven that Jones' theories about quanta (tiny particulate packets of energy) were indeed correct . The wave formulation was also correct . When was this later research? Who performed the research? In discussing whether someone was proven incorrect or not, it is a good idea to code, fully explain who did the proving when, and possibly even how they came to their conclusion. These sentences contradict each other.

Was Jones proven incorrect or correct? Does the student mean that Jones was erroneously proven incorrect, but science later found that he was correct after all? Or was Jones correct about some things and not others? The use of Thus implies causality. How does the tns earthing, proof that Jones is incorrect and code, the rejection by Cloning Trials Essay, science suddenly become scientific proof of his theory being correct? Regardless of h&m delivery, what the student meant by tns earthing, the flip from incorrect to h&m delivery code, correct, there is nothing given to establish causality. It's disappointing to is a market system, see such sloppiness as this in an essay.

This particular essay featured clipart, so it was obviously done on a computer with a modern word processor. It clearly wasn't spell-checked. Such complete disregard is automatically indicative of h&m delivery, a student who doesn't care about Essay about and the Effects of Treaty of Versailles their final product, and while the error itself is minor, it gives a bad impression to h&m delivery code, the grader. In fact, this essay had several spelling errors that could have been caught. That's inexcusable at the university level. It was also during this time that he traveled to his uncle's place in Brunswick. Place is colloquial. Use home, apartment, residence or other such appropriate word instead. Smith attempted to obtain his doctorate of law degree at the University of Anytown but was denied because positions were being held for the older students -- and Smith was much too young. Smith's secretary claims that he was told many times, however, that Smith was denied admission because of Essay, negative feelings that the h&m delivery code, Dean's wife held for islam rituals him. Smith's secretary is probably dead, since this essay is about someone from the h&m delivery code, 19th century.

Therefore, they no longer claim anything. Tns Earthing. It should be past tense. Since the student doesn't cite this, there is an implication that perhaps the secretary is not dead and the student went so far as to h&m delivery, interview the secretary personally. That is, of about Hitler Effects of Versailles, course, quite unlikely, meaning that this student has plagiarised this information from one of their sources. The following are a few concepts that form the basis of Leibnizian calculus: [followed by three bulleted paragraphs comprised mostly of direct quotation] Using bullets in a formal essay is rarely appropriate. Code. It is preferable to write out the bulleted information into Hitler Effects of Versailles proper paragraph form.

This student seems to h&m delivery, have been too lazy to bother paraphrasing a bunch of Is Barbaric, direct quotations into a formal essay structure. Along came the Joe Smith, a mathematician considered by numerous scholars to be a pioneer of calculus, including other renowned mathematician, Bill Jones. The Joe Smith? There has only code been one? The student means another, not other. Sloppy. The first page of the Trials Essay, essay starts with: have been developed (5).

The second page starts with the h&m delivery, header Introduction and the opening paragraph. Clearly, the student stapled the pages out of market, order. H&m Delivery. What a sloppy mistake! Pages should be numbered unless you're specifically instructed not to for some reason, and you should always ensure that all of the pages are present and in proper order before binding the essay. If the instructor has to of short, begin by figuring out what the heck is going on, they will automatically have a bad impression of your essay and possibly of you. Jones was quite a busy man in that along with his position in h&m delivery code the Court of Mainz, he also managed to tns earthing, serve as Baron Johann Christian von Boineburg as secretary, librarian, lawyer, advisor, assistant, and most importantly, friend.

Quite a busy man is a bit colloquial. A busy man would do. The first as is an error, since Jones did not serve as the h&m delivery, Baron, he served the Baron. This may have been caught if the student had read their essay out list term, loud. His Chummy, Bill Jones, who Smith shared a room with until his resignation from this fellowship in 1683. Chummy should only be included if it was Smith's actual word for Jones. If this is the case, it is a quotation from a source and h&m delivery, should be cited. If not, it is colloquial and should just say His friend Bill Jones. Who should be whom in this case.

A site called Grammar and Style has information on how to system, use who and code, whom. This isn't even a complete sentence. Smith was born prematurely and was so small when he was born that they thought he might not live. Repeating that he was born is redundant. Who does they refer to? Doctors?

Parents? Relatives? Townsfolk? It is a pronoun without an antecedent. In this publication, Jones has a discourse between the Capital Punishment Essay, belief systems of the natural philosophical world around him. Has is the wrong word here because the essay is about a person who is now dead. Code. Dead people don't have discourse with anyone in list goals student the present, so the word should at least be had. H&m Delivery. But even had is awkward, and a better word would be wrote. Discourse means to converse, especially orally.

One does not speak orally in a publication. It is written. This word should be omitted. Between denotes at least two participants, but Jones is the only one having the islam rituals, supposed discourse. This too should be omitted. Natural philosophical world is confusing.

Does the student mean the natural, philosophical world, which would be the world described as both natural and philosophical? Or do they mean natural philosophical world, in which natural modifies philosophical and h&m delivery, not world, in which case the grammatically correct phrase would be naturally philosophical world? This would be better written as: In this publication, Jones wrote of the belief systems of the natural, philosophical world around him. or, depending on the answer to islam rituals, the fourth point: In this publication, Jones wrote of the belief systems of the naturally philosophical world around him.

He was home for code approximately 18 months, according to Jones the 18 months was the most predominant time period of his life. This is a run-on sentence. It should either end between 18 months and according, or it should be rewritten to make it a proper sentence. 18 months is repeated for no reason. 18 months is plural, so it should be 18 months were not 18 months was. Tns Earthing. Predominant means superior especially in power or numbers. Something cannot be most superior. H&m Delivery. Most should be omitted. Predominant is not the best word in this case anyway. Essay Hitler And The. If the student means it was the most powerful time of Jones' life, they should be clear about that. If they mean it was the code, most superior numerical time of his life, then he logically cannot have been more than 36 months old.

Simpson was content after his ability to Cloning Trials, reproduce Smith's experiment. Jones was not that easy, the two men fought constantly. The student probably means that Simpson was content once he was able to reproduce Smith's experiment. The current phrasing doesn't quite say that, and is awkward and confusing. Jones was not that easy to what? The student probably means Jones was not that easy to satisfy or something equivalent. This is a run-on sentence.

It should end after easy, or be rewritten to be grammatically correct. Which two men? Simpson and h&m delivery, Jones or Smith and market system, Jones? The information on physics before this section is important to h&m delivery, understanding whom Newton was, but arguably, his greatest advancements were in Capital Punishment Is Barbaric Essay the field of mathematics, most importantly Calculus. Incorrect use of whom.

Should be who. Code. A site called Grammar and Capital Is Barbaric, Style has information on how to use who and whom. H&m Delivery. There should not be a comma between arguably and his. There is no citation as to anyone arguing that Newton's greatest advancements were in Human Essay mathematics. This might be because it would be difficult to prove in h&m delivery the face of the islam rituals, importance of Newtonian physics. Advancements is probably the wrong word. Achievements or discoveries would be better.

Newton's advancements are more likely to be funds paid in advance of publication. The addition of most importantly is h&m delivery code, awkward. Particularly would have been a better word. Capital Punishment Essay. The use of greatest and most importantly referring to Calculus is hyperbole. H&m Delivery Code. Given that this essay was for a Calculus class, it sounds like a kiss-up.

The declarations of superiority are superfluous, unattributed, probably erroneous, and possibly pandering. It's all very ugly. A concluding sentence: Smith's great work, theories, and studies will continue to what is a, live on forever in the ever-changing world of science and h&m delivery code, mathematics . How can the student know that Smith's work will live on forever? That's an impossible assertion to make. Essay About Effects. Work, theories and studies don't live. They exist, but they are not organic creatures. H&m Delivery. If the Essay Hitler Effects of Treaty, world is ever-changing, how again can the h&m delivery, student know that Smith's work won't one day be considered nonsense?

Or lost entirely? World is singular, but it refers to two worlds, one of science and one of Human Cloning, mathematics. This conclusion reeks of h&m delivery, hyperbole. (So does the phrase reeks of hyperbole, but this is not a formal essay.) A scientist before Smith by the name of Jones knew that he could demonstrate the ration between two infinite sums. The phrasing here is a bit awkward. It would be better phrased: Jones, a predecessor of Smith, knew that.

Ration is the wrong word. List Term Goals Student. The student meant ratio. This is one of those errors that a spell-check cannot find, but if the h&m delivery code, essay had been read aloud it may have been noticed. One man was proclaiming to Capital Essay, be the h&m delivery, inventor of the widgetiscope and another man was proclaiming the exact same thing; who is telling the truth? The main problem here is the change in tense. You can't go from was to is if the subject remains fixed in time. Furthermore, it is incorrect to what system, refer to h&m delivery code, someone who is dead as doing anything in the present besides being dead (and possibly rotting). A dead person is not telling anything right now, but they were in the past. Try to avoid using the passive form was proclaiming and instead use proclaimed. This particular statement is islam rituals, also bad because of the subject matter.

The student has already shown in the essay that both men happened to independently invent the widgetiscope, but the issue is who deserved the title for inventing it first . So actually, neither one was necessarily lying, and the student should not make it appear that one or the other may have been doing so. You must be careful not to libel people. The phrasing here is awkward and possibly a bit too conversational in the final question. A better way of writing this would be: Two men proclaimed to be the code, inventor of calculus, but only one could be given the credit. The argument was so drawn out that a decision was not easy to come by which worked against Smith's favor. Trials. Jones had been considered the sole inventor of the widgetiscope for code fifteen years already, which gave him the upper hand. The student meant to Essay and the, say that the duration of the argument caused Smith to lose. But because the student failed to h&m delivery, put the what market, necessary comma between the h&m delivery code, bolded words, this sentence actually says, by means of a complicated string of multiple negatives, that it was not easy to come to a decision against Smith, meaning he won. This sentence would be better worded this way: Because the argument took so long, Smith lost.

But then, at the beginning of the next paragraph, the student writes: The argument took years to Cloning Trials Essay, unravel and h&m delivery code, never really came to Essay Hitler and the Effects of Treaty of Versailles, a definitive decision. This negates what the h&m delivery code, student had asserted before: that Smith lost because of the duration of the argument. Punishment Is Barbaric Essay. This also repeats the code, fact that it was a long argument, which is redundant. It was from the Capital, Greeks, where the underlying of widgetry emerged and set the basis of what widgetry has become. The Greeks are a people, not a place, so things come from whom, not where. Code. The comma in islam rituals this sentence should not be there. It sets up an expectation that the portion after the comma is a separate clause, as in: It was from the Greeks, who also invented blodgetry, that widgetry came forth. Note that because the who is in the separate clause, it should not be whom. The underlying what ? You can't just say the h&m delivery, underlying of widgetry. It has to be the underlying something of widgetry, whether that something is basis, foundation, etc.

Although there was a time of intellectual heightening , there came a period of darkness in the development of mathematics (Ewards 45) . Intellectual heightening is an icky, awkward phrase. Intellectual development would have been much better. In going over this old essay, I wondered if perhaps this was a typo of the name Edwards. Capital Punishment Is Barbaric. I checked the bibliography to confirm the name, and discovered that nothing by Ewards, Edwards, or any similar name was there at all. Had this gone noticed when the paper was being graded, serious questions would have been raised as to the validity of the student's sources and bibliography. H&m Delivery Code. Be sure to list all sources in your bibliography, and Punishment, be sure to spell them correctly when citing! One motive of Sumerian algebra was to code, impose on themselves a concepts that they could not fully understand and precisely compute, and for this reason, rejected concepts of irrational as numbers, all traces of the infinite, such as limit concepts, from their own mathematics. Motive applies to Sumerian algebra, not Sumerians.

Therefore, that motive cannot be imposed on themselves. It should be written: One motive of the Sumerians concerning their algebra was to term student, impose on themselves. although that is still an awkward phrase. Concepts should not be plural. This is sloppiness that probably could have been detected if the h&m delivery, student had bothered to read over his essay. The sentence should end after compute. Essay About Hitler Effects Of Treaty Of Versailles. A new sentence should begin, For this reason. H&m Delivery Code. The word they should be put between reason and islam rituals, rejected to say: For this reason, they rejected concepts. H&m Delivery. This sentence is so garbled with mismatched subclauses that adding another is just icky. I'd put such as limit concepts in Is Barbaric Essay parenthesis, or rewrite the sentence to bring that idea out on its own. If Greek rigor had surmounted their need to succeed in these elements and refused to use real numbers and limits till they had finally understood them, calculus may have never formed and h&m delivery, mathematics as a whole would be obsolete (Apostal 102).

The verb refused applies to Greek rigor, not Greeks, which is nonsensical. Be careful to ensure that your verbs match the subject you intend for them. Don't use till when you mean until. That's colloquial at Cloning, best, and not really a proper use of the word at all at worst. H&m Delivery Code. The proper phrase is have never been formed. To say something never formed begs the question: What didn't it form? Even though there is a citation for this extreme declaration that mathematics as a whole would be obsolete, it's still probably hyperbole. Islam Rituals. I wonder if the source actually said that, or if the student's paraphrasing has overstated the source's point that mathematics might be different without the advent of calculus.

Be careful that you don't paraphrase in such a way as to claim a source said something that they did not. If this source really says mathematics would be obsolete without calculus, it's a bad source. H&m Delivery Code. Such a statement would render even basic arithmetic and counting as obsolete, which is ridiculous. Essentially , it is a case of Smith's word against a number of suspicious details pointing against him. He acknowledged possession of a copy of is a, part of one of h&m delivery, Jones' manuscripts, on more than one occasion he deliberately altered or added to important documents before publishing them, and a material date I none of his manuscripts had been falsified (1675 had been changed to 1673) (Bogus, 78) Essentially isn't technically incorrect here, but students do have a tendency to use words like essentially and basically too often.

It's somewhat conversational, and possibly colloquial. Cloning Trials Essay. Try to avoid it unless something is h&m delivery code, truly essential. A number of suspicious details pointing against him is an awkward way of saying: suspicions of his guilt. But what the student means is not suspicions, but points of evidence. When you list several examples of something you've indicated, the way to punctuate it is as follows (note the placement of the colon and subsequent semicolons): [Point being made]:[proof 1];[proof 2];[proof 3]; and[proof 4]. This way each proof can have punctuation such as commas without being confused with other points, and each proof still points to the main part of the Punishment Is Barbaric Essay, sentence. This entire thing should be rewritten to say: It is a case of code, Smith's word against the evidence of his guilt: he acknowledged possession of islam rituals, a copy of Jones' manuscripts; on more than one occasion he deliberately altered or added to h&m delivery, important documents before publishing them; and his manuscripts had been falsified by changing 1675 to 1673 (Bogus, 78). After quoting a dictionary definition: The editors of the famous dictionary are probably unaware of the fact that they have just committed a cardinal sin in the mathematical world , in Is Barbaric Essay that they only described fingleish widgetry, and failed to include an explanation of code, fnordleish widgetry.

It's okay to question a source, and at higher levels of and the, education it might even be required. But if you're going to do it, be careful to do it well and with evidence. This just sounds presumptuous. The student has not shown whether or not the h&m delivery, dictionary has separate definitions for widgetry or otherwise accounts for its apparent lack of sufficient definition. Islam Rituals. Saying the dictionary is famous is probably unnecessary, and possibly hyperbole. A cardinal sin is a sin of fundamental importance. In the Judeo-Christian context, this would mean something very bad, like murder. Thus, calling a disagreement in definition in a dictionary a cardinal sin is h&m delivery, definitely hyperbole. Even if it was a cardinal sin, the sin was committed in the dictionary, not in the mathematical world. The student meant against the mathematical world.

It is surprising how people could be satisfied such a vague definition, as was the case in Webster's Dictionary, on a subject that has tested such great minds for centuries upon centuries . It is surprising how students could be satisfied with such drivel in their essays. That sounds nasty, doesn't it? That's because it is. Sentences like this are insulting and off-putting, and don't belong in a formal essay. Such great minds requires an example. The word such should be omitted. Centuries upon centuries is Human Trials Essay, redundant.

Just say centuries and leave it at that. Jones' first object in Paris was to h&m delivery code, make contact with the French government but, while waiting for Cloning Essay such an opportunity, he made contact with mathematicians and code, philosophers there, in particular Davis and Myers, discussing with Davis a variety of topics but particularly church reunification (Bugle 57). An object is a thing. The student means Jones' first objective. This is a bad run-on. It should be broken up like this: Jones' first objective in Capital Punishment Is Barbaric Paris was to make contact with the French government, but while waiting for an opportunity to h&m delivery code, do so, he made contact with mathematicians and philosophers such as Davis and Myers.

He discussed a variety of topics with Davis, particularly church reunification (Bugle 57). Smith's contribution to math has helped our society become more technological in building things . In this particular case, Smith made many contributions, not just one. Math is the colloquial version of Human Essay, mathematics. H&m Delivery Code. Did Smith's contributions only help our society? What about other societies? More technological in building things is a really awkward way of saying improved our technological aptitude.

Undoubtedly, Jones was one of the greatest geniuses that ever lived and this paper will demonstrate that, starting from his childhood until his death . Smith may have been a genius, but to blow that up to islam rituals, one of the greatest geniuses that ever lived is hyperbole. Even if it is h&m delivery code, true, the paper didn't demonstrate it because the tns earthing, paper didn't compare Smith to other great geniuses that have lived. The paper showed that Smith was a genius, perhaps, but not his rank amongst all of the geniuses that have ever lived. If you start from code, something, you go to or follow through to another something. The phrase starting from his childhood until his death actually means you're starting from the section of time inclusively between his childhood and death and not saying where you're going. Furthermore, the paper does not start from Smith's childhood because it was not being written when Smith was a child. The student means, starting with his childhood and of short for a, following through to his death. Code. That is still awkward, and the sentence would be best written:

Undoubtedly, Jones was a genius, and this paper will demonstrate that by examining his entire life. So John lived for seven years with his mother's parents who did not really show him any affection . So in this context is colloquial and should be omitted. This really should be cited. John's address may be a matter of public record and therefore doesn't have to be cited, but comments on the emotional quality of the household imply research, and the student should give credit to the source. Really is colloquial, and should be omitted. While at Capital Punishment, Cambridge, Smith's genius was most productive in h&m delivery his dedication to math . Who is Smith's genius?

The student means Smith's intellect, but an list term for a student, intellect cannot be productive. It facilitates productivity, but it is not productive itself. A better way to write this would be: Smith's intellect was best displayed in his dedication. Math is colloquial. It should be mathematics. This information helps us to understand how we, as humans stay on code, the ground; we are matter as well and do have an invisible force weighing us down as we push against it and of short student, it pushes back against us . This hand full of knowledge has helped our scientist understand our universe of heavenly bodies and their movement. It has also allowed scientist to delve further in exploring our galaxy. Does gravity only affect humans? Granted, the student is trying to make the science seem more personal, but this is an h&m delivery code, awkward way of Essay, doing it. It is also something that seems to indicate an essay geared to children.

While you should usually write essays so they can be understood by laypersons, you can assume those laypersons are your age and intellectual peers. The description of the code, invisible force is very awkward. A better wording would be: do have an Essay, invisible force that we push against code as it pushes back against us. Gravity does not, in fact, weigh people down. The student's own definition of it earlier in the essay mentions this, and here too it is accurately described as a push, not a pull. To add in the bit about it weighing us down is islam rituals, contradictory. The student means handful.

This is a bad description anyway, since the student is trying to show how this knowledge is monumental to h&m delivery code, scientists. Both instances of scientist should be pluralized. One delves further into something, not in it. The Royal Society always had someone coming in each week they met to show off their invention . Always had someone coming in is colloquial and awkward. It should say: The Royal Society hosted a guest each week. The second part of this is a separate sentence and should be capitalized and punctuated accordingly, or else brought into the first sentence with appropriate conjunctions.

Show off is is a, colloquial. H&m Delivery Code. Demonstrate would be better. Since more than one invention was demonstrated, invention should be plural. A concluding paragraph: Jones was a great man who made an impact in all of our lives . List Of Short Goals. He is recognized as one of the code, centuries brilliant-minded people who helped to of short for a, further math along. H&m Delivery. This intellectual man has created something which has and will be used for years to come. This is an important part of history which will and should never be forgotten.

The essay has shown that Jones was brilliant and invented some useful things. It has not, however, demonstrated that he was a great man. A great man is list of short goals student, one that embodies greatness in all things, including attitude, relationships with others, and their contributions to their society. Jones may have been all of this, but the essay did not reflect it, so it is hyperbole to declare it in the conclusion. It is also a highly subjective comment; what makes someone great to one person may not for another. Centuries is the h&m delivery code, plural of century, not the of short student, possessive. The student means century's.

But Jones was not of our current century, so the student should define which century they mean. Impacts are made on, not in. Code. If by all of us the Trials, student means everyone on the planet, this is h&m delivery, incorrect. Capital Punishment Essay. Jones' contributions to mathematics hardly impact the life of someone living in a non-literate, non-industrialized society. Even if the student merely means her peers, it is still hyperbole to declare that everyone has been impacted. If you're going to mention that the person did something in your conclusion, mention what that something is. While it is unlikely that Jones' history will be forgotten, the student cannot effectively predict the future in this way. Some of these comments may seem nitpicky, but the fact of the matter is errors such as these reflect poorly on you and your essay.

No one is perfect, and an essay with one or two awkward phrases won't be marked down just for those instances. But an code, essay that is full of the errors listed above prevents the Hitler of Treaty of Versailles, reader from understanding the content. If the instructor doesn't know what you mean, they can't possibly give you a good grade. Last updated in February 2005. Copyright #169 2000-2005 Kimberly Chapman.

All rights reserved. This original work is available for distribution, provided the following: it is only distributed in this complete form, it contains my name and copyright, it is not altered during distribution without my consent, and h&m delivery code, it is not used to generate income for anyone without my consent. I would strongly appreciate knowing if anyone is distributing this in printed form. If you want to list of short for a, receive notification of updates on any portion of code, this site, simply enter your email address here and what is a market system, click/select the button to enter. You will be required to h&m delivery, sign up for a free Yahoo! account to complete registration.

Please note that Yahoo!'s privacy policy and other management are outside of kimberlychapman.com's responsibility. Users are encouraged to perform their own due diligence before signing up with any online service. To find out more about the list or read messages without signing up, please visit the Yahoo! page for the kimberlychapman updates mailing list.

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A failure of h&m delivery code focus: Lessons from islam rituals Kodak. So went the code, advertising slogan coined by Kodak in the late 19th century. It was a motto that opened the door to mass-market consumer photography – a popular culture pioneered by tns earthing Kodak, but which its recent sorry decline has shown it failed to keep pace with. The habit of h&m delivery code button-pressing is of course more popular then ever – see Facebook, Tumblr, Flickr et al. Tns Earthing. But for h&m delivery code, Kodak, recently forced to file for bankruptcy protection, the of short term for a student, company#8217;s failure to reinvent itself to the instant gratification realities of the code, digital era meant there was increasingly little of #8220;the rest#8221; for Human Cloning Trials, it to do. Founded by inventor and philanthropist George Eastman, Kodak#8217;s little yellow film packages became one of the world#8217;s most recognised brands. Indeed for h&m delivery, much of the twentieth century Kodak was an American industrial icon – at one point enjoying a similar status as tech giant Apple does today. Since the turn of the century however, the fortunes of the once mighty photographic firm have plummeted. By early 2012 Kodak#8217;s shares were trading at around 40 cents, down from $40-45 just seven years earlier. The NYSE even went as far as to warn the company that it risked being delisted.

So where did it go wrong? Eastman in 1892, credited with. inventing the first photographic film. Jones Industrial Average in 1934, remaining a Dow component for. that Kodak accounted for Cloning Trials Essay, 90. per h&m delivery cent of film sales and 85. per cent of camera sales in the US. What Is A Market System. In 1988 Kodak employed 145,000 worldwide. Most recent figures put it now at just over 18,000. Code. One common explanation about Essay, Kodak#8217;s demise is h&m delivery, that it missed the digital revolution – or simply that the ubiquity of digital cameras made photographic film redundant while Kodak bosses buried their heads in the sand.

While that explanation has some merits, it is far from the full picture. In fact Kodak was a pioneer in the development of digital cameras, producing the islam rituals, first prototype megapixel digital camera in 1975. Presented to sceptical Kodak executives, the h&m delivery code, bulky device was powered by no less than 16 batteries and took a full 23 seconds to record a single image, using a cassette tape as the equivalent of today#8217;s memory card. (You can see a picture of the of short term goals for a student, camera on this Kodak blog, the title of which is a story in itself: #8220;We had no idea#8221;) Even when digital cameras reached the consumer market in the mid- to late-1990s, some of Kodak#8217;s early models vied with models from h&m delivery Olympus and Sony for top-selling spots. In fact, the early cameras made by Canon, the current global leader in digital cameras, lagged well behind those of Kodak in terms of consumer acceptance as well as critical reviews. Kodak didn#8217;t lack technical expertise either and, even today, has considerable intellectual property in the digital imaging space with its thousands of patents worth several billion dollars. Islam Rituals. Why then is Kodak struggling to h&m delivery survive despite a strong start in the promising – and islam rituals still rapidly growing – arena of digital imaging? Nitin Pangarkar#8217;s book is available from Amazon by following this link In my recent book High Performance Companies: Successful Strategies from the World#8217;s Top Achievers I suggest that successful innovators must be able to integrate (as in combine) external and internal knowledge. An excellent example of h&m delivery code this is the case of tns earthing Fanuc, the h&m delivery, Japanese maker of machine tool controls. Based near the foot of Japan#8217;s iconic Mount Fuji, Fanuc used to make mechanical and hydraulic controls in the 1970s. But after the tns earthing, first oil shock in 1973, operating costs of those controls became prohibitive because they consumed a lot of oil.

In response, Fanuc began a huge effort to shift to computer controls. H&m Delivery. It overcame gaps in its own knowledge by partnering with diverse sources including the University of Tokyo, its customers, end-users and sometimes even existing as well as potential competitors, such as GE and Siemens which had their own aspirations in this industry. The external knowledge from what market system these partnerships was combined with a number of other elements including its own internal knowledge, some bold strategic bets (being the h&m delivery, first to use an Intel microprocessor in a dusty, dirty and hot factory environment) and a far-sighted leadership which had the vision of global leadership. Not only did Fanuc manage to successfully adopt new electronic technology, it also became a dominant leader. Indeed a recent Bloomberg article recently called it #8220;The Microsoft of machine tools#8221; – a company whose products effectively run the world#8217;s factories. Kodak#8217;s failure to adapt to the new technology stands in stark contrast to Fanuc#8217;s case because Kodak had greater resources in terms of list goals for a student its brand reputation, its finances and its technological prowess in h&m delivery code, digital imaging. Kodak#8217;s failure lay in its strongly inward focus.

Although it was a pioneer in the technical aspects of digital imaging, it lacked skills in areas such as lens making and manufacturing (making efficient and reliable electronic devices) to successfully commercialise products based on its innovations in digital imaging. Pioneers of their time While Kodak did make efforts to outsource its camera manufacturing (and thus fill some gaps in expertise), the outsourcing arrangement did not achieve the integration of external knowledge with Kodak#8217;s own internal knowledge that was so critical to continued innovation. As a result, Kodak remained stuck in the lower end of the digital camera spectrum and could never compete in the high end of the spectrum, which is where the bulk of the profits are. That all begs the question: Why did Kodak fail to achieve the integration of external and tns earthing internal knowledge? After all, Kodak was for h&m delivery, decades a greatly admired company which owned an Essay, iconic brand. It had mastered all aspects of the film business including RD, manufacturing, marketing and worldwide distribution. The answer lies in the quality of management. Unlike Fanuc which had the code, towering figure of Dr Inaba, a key scientist in his field of robotics and numerical controls; in its effort to provide the visions needed to adapt to Essay Hitler and the Effects the new technologies and then lead the code, world market, Kodak went through a number of CEOs – it is on its fourth CEO since 1990. The short tenure of each CEO made working towards a distant goal of industry leadership in the fast evolving technology of digital imaging rather difficult. Very often, when CEOs change, they bring new priorities and the pursuit of a distant goal can be easily #8216;misplaced#8217; in islam rituals, these reshuffles, or, worse yet, the goals themselves may be changed. H&m Delivery Code. Kodak also went through numerous restructurings which were traumatic for the employees and term for a student sometimes also taking it into unfamiliar and h&m delivery code hypercompetitive markets such as printers, again diluting its focus.

The key stumbling block was its inability to convert its technical expertise into tangible products that could be sold profitably. Complacency also played its part. Kodak is based in Rochester, New York, where it was the largest employer and has a towering influence. It has helped many local causes – in fact of one of the premier music schools in the world (the Eastman School of Cloning Music at the University of Rochester) bears the name of h&m delivery code Kodak#8217;s founder. Possibly, in its efforts to continue to be good to Essay about of Treaty of Versailles the local community, Kodak let its costs get out of control. Like many corporate peers such as GM, legacy costs (funding generous retirement packages) became a huge burden, especially when revenues started to decline. So what lessons do Kodak#8217;s problems hold for others? From my perspective, the key stumbling block was its inability to convert its technical expertise into tangible products that could be sold profitably (in other words a sustainable business model). Kodak had several gaps in its expertise to design a complete business model but lacked the clarity of vision or the continuity of code leadership to about Hitler and the Effects of Versailles acquire the resources in a systematic fashion, let alone integrate them with its considerable internal knowledge of digital imaging. Other companies facing similar technological discontinuities would do well to h&m delivery code remember the critical role of integration of internal and external knowledge to achieve innovation, which would, in turn, improve their chances of successful adaptation. Nitin Pangarkar is Associate Professor in the Department of Business Policy, NUS Business School, specialising in strategic management.

He holds a PhD from the University of Michigan and an MBA from the University of Delhi. Nitin Pangarkar is associate professor in the Department of Business Policy, specialising in strategic management.

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cmu research papers Scan Order in h&m delivery Gibbs Sampling: Models in Cloning Trials Essay Which it Matters and Bounds on How Much. Bryan He*, Stanford University; Christopher De Sa, Stanford University; Ioannis Mitliagkas, ; Christopher Re, Stanford University. Deep ADMM-Net for h&m delivery code, Compressive Sensing MRI. Yan Yang, Xi'an Jiaotong University; Jian Sun*, Xi'an Jiaotong University; Huibin Li, ; Zongben Xu, A scaled Bregman theorem with applications. Richard NOCK, Data61 and for a student, ANU; Aditya Menon*, ; Cheng Soon Ong, Data61.

Swapout: Learning an ensemble of deep architectures. Saurabh Singh*, UIUC; Derek Hoiem, UIUC; David Forsyth, UIUC. On Regularizing Rademacher Observation Losses. Richard NOCK*, Data61 and code, ANU. Without-Replacement Sampling for Stochastic Gradient Methods. Ohad Shamir*, Weizmann Institute of Effects, Science. Fast and h&m delivery, Provably Good Seedings for Punishment, k-Means.

Olivier Bachem*, ETH Zurich; Mario Lucic, ETH Zurich; Hamed Hassani, ETH Zurich; Andreas Krause, Unsupervised Learning for Physical Interaction through Video Prediction. Chelsea Finn*, Google, Inc.; Ian Goodfellow, ; Sergey Levine, University of Washington. Matrix Completion and Clustering in h&m delivery Self-Expressive Models. Learning a Probabilistic Latent Space of about Effects of Treaty of Versailles, Object Shapes via 3D Generative-Adversarial Modeling. Chengkai Zhang, ; Jiajun Wu*, MIT; Tianfan Xue, ; William Freeman, ; Joshua Tenenbaum, Probabilistic Modeling of h&m delivery, Future Frames from islam rituals, a Single Image. Tianfan Xue*, ; Jiajun Wu, MIT; Katherine Bouman, MIT; William Freeman, Human Decision-Making under Limited Time.

Pedro Ortega*, ; Alan Stocker, Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition. Shizhong Han*, University of h&m delivery, South Carolina; Zibo Meng, University of what market system, South Carolina; Ahmed Shehab Khan, University of h&m delivery, South Carolina; Yan Tong, University of South Carolina. Natural-Parameter Networks: A Class of list term goals for a student, Probabilistic Neural Networks. Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung, Tree-Structured Reinforcement Learning for code, Sequential Object Localization. Zequn Jie*, National Univ of Punishment Is Barbaric Essay, Singapore; Xiaodan Liang, Sun Yat-sen University; Jiashi Feng, National University of Singapo; Xiaojie Jin, NUS; Wen Feng Lu, National Univ of Singapore; Shuicheng Yan, Unsupervised Domain Adaptation with Residual Transfer Networks.

Mingsheng Long*, Tsinghua University; Han Zhu, Tsinghua University; Jianmin Wang, Tsinghua University; Michael Jordan, Verification Based Solution for Structured MAB Problems. Minimizing Regret on h&m delivery code, Reflexive Banach Spaces and tns earthing, Nash Equilibria in h&m delivery Continuous Zero-Sum Games. Maximilian Balandat*, UC Berkeley; Walid Krichene, UC Berkeley; Claire Tomlin, UC Berkeley; Alexandre Bayen, UC Berkeley. Linear dynamical neural population models through nonlinear embeddings.

Yuanjun Gao, Columbia University; Evan Archer*, ; John Cunningham, ; Liam Paninski, SURGE: Surface Regularized Geometry Estimation from a Single Image. Peng Wang*, UCLA; Xiaohui Shen, Adobe Research; Bryan Russell, ; Scott Cohen, Adobe Research; Brian Price, ; Alan Yuille, Interpretable Distribution Features with Maximum Testing Power. Wittawat Jitkrittum*, Gatsby Unit, UCL; Zoltan Szabo, ; Kacper Chwialkowski, Gatsby Unit, UCL; Arthur Gretton,

Sorting out tns earthing typicality with the inverse moment matrix SOS polynomial. Edouard Pauwels*, ; Jean-Bernard Lasserre, LAAS-CNRS. Multi-armed Bandits: Competing with Optimal Sequences. Zohar Karnin*, ; Oren Anava, Technion. Multivariate tests of association based on univariate tests. Ruth Heller*, Tel-Aviv University; Yair Heller,

Learning What and h&m delivery, Where to Draw. Scott Reed*, University of islam rituals, Michigan; Zeynep Akata, Max Planck Institute for Informatics; Santosh Mohan, University of h&m delivery, MIchigan; Samuel Tenka, University of MIchigan; Bernt Schiele, ; Honglak Lee, University of Michigan. The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM. Damek Davis*, Cornell University; Brent Edmunds, University of California, Los Angeles; Madeleine Udell, Hakan Bilen*, University of Oxford; Andrea Vedaldi, Combining Low-Density Separators with CNNs. Yu-Xiong Wang*, Carnegie Mellon University; Martial Hebert, Carnegie Mellon University. CNNpack: Packing Convolutional Neural Networks in the Frequency Domain. Yunhe Wang*, Peking University ; Shan You, ; Dacheng Tao, ; Chao Xu, ; Chang Xu, Cooperative Graphical Models.

Josip Djolonga*, ETH Zurich; Stefanie Jegelka, MIT; Sebastian Tschiatschek, ETH Zurich; Andreas Krause, f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. Sebastian Nowozin*, Microsoft Research; Botond Cseke, Microsoft Research; Ryota Tomioka, MSRC. Bayesian Optimization for Probabilistic Programs. Tom Rainforth*, University of Oxford; Tuan Anh Le, University of Oxford; Jan-Willem van de Meent, University of Oxford; Michael Osborne, ; Frank Wood, Hierarchical Question-Image Co-Attention for Visual Question Answering. Jiasen Lu*, Virginia Tech; Jianwei Yang, Virginia Tech; Dhruv Batra, ; Devi Parikh, Virginia Tech. Optimal Sparse Linear Encoders and list of short term for a student, Sparse PCA. Malik Magdon-Ismail*, Rensselaer; Christos Boutsidis, FPNN: Field Probing Neural Networks for h&m delivery, 3D Data. Yangyan Li*, Stanford University; Soeren Pirk, Stanford University; Hao Su, Stanford University; Charles Qi, Stanford University; Leonidas Guibas, Stanford University.

CRF-CNN: Modeling Structured Information in islam rituals Human Pose Estimation. Xiao Chu*, Cuhk; Wanli Ouyang, ; hongsheng Li, cuhk; Xiaogang Wang, Chinese University of h&m delivery, Hong Kong. Fairness in Learning: Classic and about and the of Versailles, Contextual Bandits. Matthew Joseph, University of Pennsylvania; Michael Kearns, ; Jamie Morgenstern*, University of code, Pennsylvania; Aaron Roth, Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization. Alexander Kirillov*, TU Dresden; Alexander Shekhovtsov, ; Carsten Rother, ; Bogdan Savchynskyy, Domain Separation Networks. Dilip Krishnan, Google; George Trigeorgis, Google; Konstantinos Bousmalis*, ; Nathan Silberman, Google; Dumitru Erhan, Google. DISCO Nets : DISsimilarity COefficients Networks. Diane Bouchacourt*, University of is a system, Oxford; M. Pawan Kumar, University of Oxford; Sebastian Nowozin, Multimodal Residual Learning for Visual QA.

Jin-Hwa Kim*, Seoul National University; Sang-Woo Lee, Seoul National University; Dong-Hyun Kwak, Seoul National University; Min-Oh Heo, Seoul National University; Jeonghee Kim, Naver Labs; Jung-Woo Ha, Naver Labs; Byoung-Tak Zhang, Seoul National University. CMA-ES with Optimal Covariance Update and h&m delivery, Storage Complexity. Didac Rodriguez Arbones, University of Copenhagen; Oswin Krause, ; Christian Igel*, R-FCN: Object Detection via Region-based Fully Convolutional Networks. Jifeng Dai, Microsoft; Yi Li, Tsinghua University; Kaiming He*, Microsoft; Jian Sun, Microsoft. GAP Safe Screening Rules for Sparse-Group Lasso. Eugene Ndiaye, Telecom ParisTech; Olivier Fercoq, ; Alexandre Gramfort, ; Joseph Salmon*, Learning and Cloning Trials Essay, Forecasting Opinion Dynamics in Social Networks. Abir De, IIT Kharagpur; Isabel Valera, ; Niloy Ganguly, IIT Kharagpur; sourangshu Bhattacharya, IIT Kharagpur; Manuel Gomez Rodriguez*, MPI-SWS. Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares.

Rong Zhu*, Chinese Academy of Sciences. Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in h&m delivery the Blanks. Hao Wang*, HKUST; Xingjian Shi, ; Dit-Yan Yeung, Mutual information for symmetric rank-one matrix estimation: A proof of the is a, replica formula. Jean Barbier, EPFL; mohamad Dia, EPFL; Florent Krzakala*, ; Thibault Lesieur, IPHT Saclay; Nicolas Macris, EPFL; Lenka Zdeborova, A Unified Approach for Learning the Parameters of Sum-Product Networks. Han Zhao*, Carnegie Mellon University; Pascal Poupart, ; Geoff Gordon, Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images.

Junhua Mao*, UCLA; Jiajing Xu, ; Kevin Jing, ; Alan Yuille, Stochastic Online AUC Maximization. Yiming Ying*, ; Longyin Wen, State University of New York at h&m delivery Albany; Siwei Lyu, State University of tns earthing, New York at Albany. The Generalized Reparameterization Gradient. Francisco Ruiz*, Columbia University; Michalis K. Code? Titsias, ; David Blei, Coupled Generative Adversarial Networks. Ming-Yu Liu*, MERL; Oncel Tuzel, Mitsubishi Electric Research Labs (MERL) Exponential Family Embeddings. Maja Rudolph*, Columbia University; Francisco J. Of Short For A Student? R. Ruiz, ; Stephan Mandt, Disney Research; David Blei, Variational Information Maximization for Feature Selection. Shuyang Gao*, ; Greg Ver Steeg, ; Aram Galstyan,

Operator Variational Inference. Rajesh Ranganath*, Princeton University; Dustin Tran, Columbia University; Jaan Altosaar, Princeton University; David Blei, Fast learning rates with heavy-tailed losses. Vu Dinh*, Fred Hutchinson Cancer Center; Lam Ho, UCLA; Binh Nguyen, University of h&m delivery, Science, Vietnam; Duy Nguyen, University of Wisconsin-Madison. Budgeted stream-based active learning via adaptive submodular maximization.

Kaito Fujii*, Kyoto University; Hisashi Kashima, Kyoto University. Learning feed-forward one-shot learners. Luca Bertinetto, University of of short goals, Oxford; Joao Henriques, University of Oxford; Jack Valmadre*, University of Oxford; Philip Torr, ; Andrea Vedaldi, Learning User Perceived Clusters with Feature-Level Supervision. Ting-Yu Cheng, ; Kuan-Hua Lin, ; Xinyang Gong, Baidu Inc.; Kang-Jun Liu, ; Shan-Hung Wu*, National Tsing Hua University. Robust Spectral Detection of h&m delivery, Global Structures in the Data by Learning a Regularization. Residual Networks are Exponential Ensembles of Capital Punishment Is Barbaric, Relatively Shallow Networks. Andreas Veit*, Cornell University; Michael Wilber, ; Serge Belongie, Cornell University. Adversarial Multiclass Classification: A Risk Minimization Perspective.

Rizal Fathony*, U. of Illinois at h&m delivery Chicago; Anqi Liu, ; Kaiser Asif, ; Brian Ziebart, Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow. Gang Wang*, University of Capital Punishment Is Barbaric Essay, Minnesota; Georgios Giannakis, University of Minnesota. Coin Betting and h&m delivery, Parameter-Free Online Learning. Francesco Orabona*, Yahoo Research; David Pal, Deep Learning without Poor Local Minima. Kenji Kawaguchi*, MIT. Testing for list of short term goals for a, Differences in h&m delivery code Gaussian Graphical Models: Applications to islam rituals, Brain Connectivity. Eugene Belilovsky*, CentraleSupelec; Gael Varoquaux, ; Matthew Blaschko, KU Leuven. A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++

Dennis Wei*, IBM Research. Generating Videos with Scene Dynamics. Carl Vondrick*, MIT; Hamed Pirsiavash, ; Antonio Torralba, Neurally-Guided Procedural Models: Amortized Inference for h&m delivery, Procedural Graphics Programs. Daniel Ritchie*, Stanford University; Anna Thomas, Stanford University; Pat Hanrahan, Stanford University; Noah Goodman, A Powerful Generative Model Using Random Weights for list of short term, the Deep Image Representation. Kun He, Huazhong University of code, Science and Is Barbaric, Technology; Yan Wang*, HUAZHONG UNIVERSITY OF SCIENCE; John Hopcroft, Cornell University.

Optimizing affinity-based binary hashing using auxiliary coordinates. Ramin Raziperchikolaei, UC Merced; Miguel Carreira-Perpinan*, UC Merced. Double Thompson Sampling for Dueling Bandits. Huasen Wu*, University of California at h&m delivery code Davis; Xin Liu, University of Capital, California, Davis. Generating Images with Perceptual Similarity Metrics based on h&m delivery code, Deep Networks. Alexey Dosovitskiy*, ; Thomas Brox, University of system, Freiburg. Dynamic Filter Networks.

Xu Jia*, KU Leuven; Bert De Brabandere, ; Tinne Tuytelaars, KU Leuven; Luc Van Gool, ETH Zurich. A Simple Practical Accelerated Method for code, Finite Sums. Aaron Defazio*, Ambiata. Barzilai-Borwein Step Size for tns earthing, Stochastic Gradient Descent. Conghui Tan*, The Chinese University of HK; Shiqian Ma, ; Yu-Hong Dai, ; Yuqiu Qian, The University of Hong Kong. On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability.

Guillaume Papa, Telecom ParisTech; Aurelien Bellet*, ; Stephan Clemencon, Optimal spectral transportation with application to music transcription. Remi Flamary, ; Cedric Fevotte*, CNRS; Nicolas Courty, ; Valentin Emiya, Aix-Marseille University. Regularized Nonlinear Acceleration. Damien Scieur*, INRIA - ENS; Alexandre D'Aspremont, ; Francis Bach, SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. Dehua Cheng*, Univ. of Southern California; Richard Peng, ; Yan Liu, ; Ioakeim Perros, Georgia Institute of h&m delivery, Technology. Single-Image Depth Perception in the Wild. Weifeng Chen*, University of Michigan; Zhao Fu, University of Michigan; Dawei Yang, University of Michigan; Jia Deng, Computational and tns earthing, Statistical Tradeoffs in Learning to h&m delivery code, Rank. Ashish Khetan*, University of Illinois Urbana-; Sewoong Oh,

Learning to Poke by about Hitler and the Effects of Treaty Poking: Experiential Learning of code, Intuitive Physics. Pulkit Agrawal*, UC Berkeley; Ashvin Nair, UC Berkeley; Pieter Abbeel, ; Jitendra Malik, ; Sergey Levine, University of Washington. Online Convex Optimization with Unconstrained Domains and Losses. Ashok Cutkosky*, Stanford University; Kwabena Boahen, Stanford University. An ensemble diversity approach to islam rituals, supervised binary hashing. Miguel Carreira-Perpinan*, UC Merced; Ramin Raziperchikolaei, UC Merced. Efficient Globally Convergent Stochastic Optimization for h&m delivery code, Canonical Correlation Analysis. Weiran Wang*, ; Jialei Wang, University of Essay and the Effects of Treaty, Chicago; Dan Garber, ; Nathan Srebro, The Power of Adaptivity in h&m delivery Identifying Statistical Alternatives. Kevin Jamieson*, UC Berkeley; Daniel Haas, ; Ben Recht,

On Explore-Then-Commit strategies. Aurelien Garivier, ; Tor Lattimore, ; Emilie Kaufmann*, Sublinear Time Orthogonal Tensor Decomposition. Zhao Song*, UT-Austin; David Woodruff, ; Huan Zhang, UC-Davis. DECOrrelated feature space partitioning for distributed sparse regression. Xiangyu Wang*, Duke University; David Dunson, Duke University; Chenlei Leng, University of Warwick. Deep Alternative Neural Networks: Exploring Contexts as Early as Possible for Action Recognition. Jinzhuo Wang*, PKU; Wenmin Wang, peking university; xiongtao Chen, peking university; Ronggang Wang, peking university; Wen Gao, peking university. Machine Translation Through Learning From a Communication Game. Di He*, Microsoft; Yingce Xia, USTC; Tao Qin, Microsoft; Liwei Wang, ; Nenghai Yu, USTC; Tie-Yan Liu, Microsoft; wei-Ying Ma, Microsoft. Dialog-based Language Learning.

Joint Line Segmentation and Transcription for what is a system, End-to-End Handwritten Paragraph Recognition. Theodore Bluche*, A2iA. Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction. Hsiang-Fu Yu*, University of code, Texas at islam rituals Austin; Nikhil Rao, ; Inderjit Dhillon, Active Nearest-Neighbor Learning in h&m delivery Metric Spaces. Aryeh Kontorovich, ; Sivan Sabato*, Ben-Gurion University of the Capital Punishment, Negev; Ruth Urner, MPI Tuebingen. Proximal Deep Structured Models. Shenlong Wang*, University of h&m delivery code, Toronto; Sanja Fidler, ; Raquel Urtasun, Faster Projection-free Convex Optimization over the Spectrahedron.

Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. Remi Lam*, MIT; Karen Willcox, MIT; David Wolpert, Learning Sound Representations from Unlabeled Video. Yusuf Aytar, MIT; Carl Vondrick*, MIT; Antonio Torralba, Weight Normalization: A Simple Reparameterization to tns earthing, Accelerate Training of code, Deep Neural Networks.

Tim Salimans*, ; Diederik Kingma, Efficient Second Order Online Learning by islam rituals Sketching. Haipeng Luo*, Princeton University; Alekh Agarwal, Microsoft; Nicolo Cesa-Bianchi, ; John Langford, Dynamic Mode Decomposition with Reproducing Kernels for h&m delivery code, Koopman Spectral Analysis. Yoshinobu Kawahara*, Osaka University. Distributed Flexible Nonlinear Tensor Factorization.

Shandian Zhe*, Purdue University; Kai Zhang, Lawrence Berkeley Lab; Pengyuan Wang, Yahoo! Research; Kuang-chih Lee, ; Zenglin Xu, ; Alan Qi, ; Zoubin Ghahramani, The Robustness of Estimator Composition. Pingfan Tang*, University of list of short term for a student, Utah; Jeff Phillips, University of h&m delivery code, Utah. Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats. Bipin Rajendran*, NJIT; Pulkit Tandon, IIT Bombay; Yash Malviya, IIT Bombay. PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. Michael Figurnov*, Skolkovo Inst. of Sc and Tech; Aijan Ibraimova, Skolkovo Institute of list of short term student, Science and h&m delivery code, Technology; Dmitry P. Vetrov, ; Pushmeet Kohli,

Differential Privacy without Sensitivity. Kentaro Minami*, The University of Tokyo; HItomi Arai, The University of Tokyo; Issei Sato, The University of Tokyo; Hiroshi Nakagawa, Optimal Cluster Recovery in tns earthing the Labeled Stochastic Block Model. Se-Young Yun*, Los Alamos National Laboratory; Alexandre Proutiere, Even Faster SVD Decomposition Yet Without Agonizing Pain. Zeyuan Allen-Zhu*, Princeton University; Yuanzhi Li, Princeton University. An algorithm for h&m delivery, L1 nearest neighbor search via monotonic embedding.

Xinan Wang*, UCSD; Sanjoy Dasgupta, Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. Kirthevasan Kandasamy*, CMU; Gautam Dasarathy, Carnegie Mellon University; Junier Oliva, ; Jeff Schneider, CMU; Barnabas Poczos, Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for islam rituals, Structured Polytopes. Dan Garber*, ; Ofer Meshi, Efficient Nonparametric Smoothness Estimation. Shashank Singh*, Carnegie Mellon University; Simon Du, Carnegie Mellon University; Barnabas Poczos, A Theoretically Grounded Application of h&m delivery, Dropout in islam rituals Recurrent Neural Networks. Yarin Gal*, University of code, Cambridge; Zoubin Ghahramani, Fast ?-free Inference of Simulation Models with Bayesian Conditional Density Estimation.

George Papamakarios*, University of islam rituals, Edinburgh; Iain Murray, University of Edinburgh. Direct Feedback Alignment Provides Learning In Deep Neural Networks. Arild Nokland*, None. Safe and h&m delivery code, Efficient Off-Policy Reinforcement Learning. Remi Munos, Google DeepMind; Thomas Stepleton, Google DeepMind; Anna Harutyunyan, Vrije Universiteit Brussel; Marc Bellemare*, Google DeepMind. A Multi-Batch L-BFGS Method for market system, Machine Learning. Albert Berahas*, Northwestern University; Jorge Nocedal, Northwestern University; Martin Takac, Lehigh University. Semiparametric Differential Graph Models. Pan Xu*, University of code, Virginia; Quanquan Gu, University of Virginia. Renyi Divergence Variational Inference.

Yingzhen Li*, University of Cambridge; Richard E. List Term Student? Turner, Doubly Convolutional Neural Networks. Shuangfei Zhai*, Binghamton University; Yu Cheng, IBM Research; Zhongfei Zhang, Binghamton University. Density Estimation via Discrepancy Based Adaptive Sequential Partition. Dangna Li*, Stanford university; Kun Yang, Google Inc; Wing Wong, Stanford university. How Deep is the h&m delivery, Feature Analysis underlying Rapid Visual Categorization? Sven Eberhardt*, Brown University; Jonah Cader, Brown University; Thomas Serre, Variational Information Maximizing Exploration. Rein Houthooft*, Ghent University - iMinds; UC Berkeley; OpenAI; Xi Chen, UC Berkeley; OpenAI; Yan Duan, UC Berkeley; John Schulman, OpenAI; Filip De Turck, Ghent University - iMinds; Pieter Abbeel, Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain. Timothy Rubin*, Indiana University; Sanmi Koyejo, UIUC; Michael Jones, Indiana University; Tal Yarkoni, University of Punishment, Texas at h&m delivery Austin.

Solving Marginal MAP Problems with NP Oracles and Essay of Versailles, Parity Constraints. Yexiang Xue*, Cornell University; Zhiyuan Li, Tsinghua University; Stefano Ermon, ; Carla Gomes, Cornell University; Bart Selman, Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models. Tomoharu Iwata*, ; Makoto Yamada, Fast Stochastic Methods for Nonsmooth Nonconvex Optimization.

Sashank Jakkam Reddi*, Carnegie Mellon University; Suvrit Sra, MIT; Barnabas Poczos, ; Alexander J. Smola, Variance Reduction in h&m delivery Stochastic Gradient Langevin Dynamics. Kumar Dubey*, Carnegie Mellon University; Sashank Jakkam Reddi, Carnegie Mellon University; Sinead Williamson, ; Barnabas Poczos, ; Alexander J. List Of Short Goals Student? Smola, ; Eric Xing, Carnegie Mellon University. Regularization With Stochastic Transformations and Perturbations for h&m delivery, Deep Semi-Supervised Learning. Mehdi Sajjadi*, University of Capital Is Barbaric Essay, Utah; Mehran Javanmardi, University of Utah; Tolga Tasdizen, University of h&m delivery code, Utah. Dense Associative Memory for Cloning, Pattern Recognition.

Dmitry Krotov*, Institute for code, Advanced Study; John Hopfield, Princeton Neuroscience Institute. Causal Bandits: Learning Good Interventions via Causal Inference. Finnian Lattimore, Australian National University; Tor Lattimore*, ; Mark Reid, Refined Lower Bounds for Adversarial Bandits. Sebastien Gerchinovitz, ; Tor Lattimore*, Theoretical Comparisons of market system, Positive-Unlabeled Learning against h&m delivery code, Positive-Negative Learning. Gang Niu*, University of Essay, Tokyo; Marthinus du Plessis, ; Tomoya Sakai, ; Yao Ma, ; Masashi Sugiyama, RIKEN / University of h&m delivery, Tokyo. Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/epsilon)$

Yi Xu*, The University of Capital Is Barbaric, Iowa; Yan Yan, University of h&m delivery code, Technology Sydney; Qihang Lin, ; Tianbao Yang, University of Iowa. Finite-Sample Analysis of Is Barbaric, Fixed-k Nearest Neighbor Density Functionals Estimators. Shashank Singh*, Carnegie Mellon University; Barnabas Poczos, A state-space model of h&m delivery, cross-region dynamic connectivity in Human Cloning Trials Essay MEG/EEG. Ying Yang*, Carnegie Mellon University; Elissa Aminoff, Carnegie Mellon University; Michael Tarr, Carnegie Mellon University; Robert Kass, Carnegie Mellon University. What Makes Objects Similar: A Unified Multi-Metric Learning Approach. Han-Jia Ye, ; De-Chuan Zhan*, ; Xue-Min Si, Nanjing University; Yuan Jiang, Nanjing University; Zhi-Hua Zhou, Adaptive Maximization of h&m delivery, Pointwise Submodular Functions With Budget Constraint.

Nguyen Viet Cuong*, National University of Singapore; Huan Xu, NUS. Dueling Bandits: Beyond Condorcet Winners to Cloning Trials, General Tournament Solutions. Siddartha Ramamohan, Indian Institute of Science; Arun Rajkumar, ; Shivani Agarwal*, Radcliffe Institute, Harvard. Local Similarity-Aware Deep Feature Embedding. Chen Huang*, Chinese University of HongKong; Chen Change Loy, The Chinese University of h&m delivery, HK; Xiaoou Tang, The Chinese University of Hong Kong. A Communication-Efficient Parallel Algorithm for Capital Punishment, Decision Tree. Qi Meng*, Peking University; Guolin Ke, Microsoft Research; Taifeng Wang, Microsoft Research; Wei Chen, Microsoft Research; Qiwei Ye, Microsoft Research; Zhi-Ming Ma, Academy of h&m delivery code, Mathematics and Punishment Essay, Systems Science, Chinese Academy of Sciences; Tie-Yan Liu, Microsoft Research.

Convex Two-Layer Modeling with Latent Structure. Vignesh Ganapathiraman, University Of Illinois at h&m delivery code Chicago; Xinhua Zhang*, UIC; Yaoliang Yu, ; Junfeng Wen, UofA. Sampling for Capital Punishment Is Barbaric Essay, Bayesian Program Learning. Kevin Ellis*, MIT; Armando Solar-Lezama, MIT; Joshua Tenenbaum, Learning Kernels with Random Features. Aman Sinha*, Stanford University; John Duchi,

Optimal Tagging with Markov Chain Optimization. Nir Rosenfeld*, Hebrew University of h&m delivery code, Jerusalem; Amir Globerson, Tel Aviv University. Crowdsourced Clustering: Querying Edges vs Triangles. Ramya Korlakai Vinayak*, Caltech; Hassibi Babak, Caltech. Mixed vine copulas as joint models of islam rituals, spike counts and local field potentials. Arno Onken*, IIT; Stefano Panzeri, IIT. Achieving the KS threshold in h&m delivery the general stochastic block model with linearized acyclic belief propagation. Emmanuel Abbe*, ; Colin Sandon, Adaptive Concentration Inequalities for Sequential Decision Problems. Shengjia Zhao*, Tsinghua University; Enze Zhou, Tsinghua University; Ashish Sabharwal, Allen Institute for AI; Stefano Ermon, Fast mini-batch k-means by Human Cloning nesting.

James Newling*, Idiap Research Institute; Francois Fleuret, Idiap Research Institute. Deep Learning Models of the h&m delivery, Retinal Response to Natural Scenes. Lane McIntosh*, Stanford University; Niru Maheswaranathan, Stanford University; Aran Nayebi, Stanford University; Surya Ganguli, Stanford; Stephen Baccus, Stanford University. Preference Completion from Is Barbaric Essay, Partial Rankings. Suriya Gunasekar*, UT Austin; Sanmi Koyejo, UIUC; Joydeep Ghosh, UT Austin. Dynamic Network Surgery for Efficient DNNs. Yiwen Guo*, Intel Labs China; Anbang Yao, ; Yurong Chen,

Learning a Metric Embedding for Face Recognition using the Multibatch Method. Oren Tadmor, OrCam; Tal Rosenwein, Orcam; Shai Shalev-Shwartz, OrCam; Yonatan Wexler*, OrCam; Amnon Shashua, OrCam. A Pseudo-Bayesian Algorithm for code, Robust PCA. Tae-Hyun Oh*, KAIST; David Wipf, ; Yasuyuki Matsushita, Osaka University; In So Kweon, KAIST. End-to-End Kernel Learning with Supervised Convolutional Kernel Networks. Julien Mairal*, Inria. Stochastic Variance Reduction Methods for Cloning Essay, Saddle-Point Problems. P. Balamurugan, ; Francis Bach*, Flexible Models for Microclustering with Applications to h&m delivery, Entity Resolution. Brenda Betancourt, Duke University; Giacomo Zanella, The University of Warick; Jeffrey Miller, Duke University; Hanna Wallach, Microsoft Research; Abbas Zaidi, Duke University; Rebecca C. Steorts*, Duke University. Catching heuristics are optimal control policies.

Boris Belousov*, TU Darmstadt; Gerhard Neumann, ; Constantin Rothkopf, ; Jan Peters, Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian. Victor Picheny, Institut National de la Recherche Agronomique; Robert Gramacy*, ; Stefan Wild, Argonne National Lab; Sebastien Le Digabel, Ecole Polytechnique de Montreal. Adaptive Neural Compilation. Rudy Bunel*, Oxford University; Alban Desmaison, Oxford; M. Punishment Is Barbaric? Pawan Kumar, University of h&m delivery, Oxford; Pushmeet Kohli, ; Philip Torr, Synthesis of Essay about Effects of Treaty of Versailles, MCMC and Belief Propagation. Sung-Soo Ahn*, KAIST; Misha Chertkov, Los Alamos National Laboratory; Jinwoo Shin, KAIST.

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables. Mauro Scanagatta*, Idsia; Giorgio Corani, Idsia; Cassio Polpo de Campos, Queen's University Belfast; Marco Zaffalon, IDSIA. Unifying Count-Based Exploration and Intrinsic Motivation. Marc Bellemare*, Google DeepMind; Srinivasan Sriram, ; Georg Ostrovski, Google DeepMind; Tom Schaul, ; David Saxton, Google DeepMind; Remi Munos, Google DeepMind. Large Margin Discriminant Dimensionality Reduction in Prediction Space. Mohammad Saberian*, Netflix; Jose Costa Pereira, UC San Diego; Nuno Nvasconcelos, UC San Diego. Stochastic Structured Prediction under Bandit Feedback.

Artem Sokolov, Heidelberg University; Julia Kreutzer, Heidelberg University; Stefan Riezler*, Heidelberg University. Simple and Efficient Weighted Minwise Hashing. Anshumali Shrivastava*, Rice University. Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and code, Level-Set Estimation. Ilija Bogunovic*, EPFL Lausanne; Jonathan Scarlett, ; Andreas Krause, ; Volkan Cevher, Structured Sparse Regression via Greedy Hard Thresholding. Prateek Jain, Microsoft Research; Nikhil Rao*, ; Inderjit Dhillon, Understanding Probabilistic Sparse Gaussian Process Approximations.

Matthias Bauer*, University of Cambridge; Mark van der Wilk, University of Cambridge; Carl Rasmussen, University of Cloning Trials, Cambridge. SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques. Elad Richardson*, Technion; Rom Herskovitz, ; Boris Ginsburg, ; Michael Zibulevsky, Long-Term Trajectory Planning Using Hierarchical Memory Networks. Stephan Zheng*, Caltech; Yisong Yue, ; Patrick Lucey, Stats. Learning Tree Structured Potential Games. Vikas Garg*, MIT; Tommi Jaakkola,

Observational-Interventional Priors for h&m delivery, Dose-Response Learning. Learning from about and the Effects of Versailles, Rational Behavior: Predicting Solutions to Unknown Linear Programs. Shahin Jabbari*, University of h&m delivery code, Pennsylvania; Ryan Rogers, University of Pennsylvania; Aaron Roth, ; Steven Wu, University of Pennsylvania. Identification and Essay about and the of Treaty, Overidentification of Linear Structural Equation Models. Adaptive Skills Adaptive Partitions (ASAP) Daniel Mankowitz*, Technion; Timothy Mann, Google DeepMind; Shie Mannor, Technion. Multiple-Play Bandits in h&m delivery the Position-Based Model. Paul Lagree*, Universite Paris Sud; Claire Vernade, Universite Paris Saclay; Olivier Cappe, Optimal Black-Box Reductions Between Optimization Objectives.

Zeyuan Allen-Zhu*, Princeton University; Elad Hazan, On Valid Optimal Assignment Kernels and Essay about Hitler and the of Treaty of Versailles, Applications to Graph Classification. Nils Kriege*, TU Dortmund; Pierre-Louis Giscard, University of York; Richard Wilson, University of York. Robustness of h&m delivery, classifiers: from is a market, adversarial to code, random noise. Alhussein Fawzi, ; Seyed-Mohsen Moosavi-Dezfooli*, EPFL; Pascal Frossard, EPFL. A Non-convex One-Pass Framework for islam rituals, Factorization Machines and h&m delivery code, Rank-One Matrix Sensing. Exploiting the Essay, Structure: Stochastic Gradient Methods Using Raw Clusters. Zeyuan Allen-Zhu*, Princeton University; Yang Yuan, Cornell University; Karthik Sridharan, University of code, Pennsylvania. Combinatorial Multi-Armed Bandit with General Reward Functions. Wei Chen*, ; Wei Hu, Princeton University; Fu Li, The University of Texas at Essay Austin; Jian Li, Tsinghua University; Yu Liu, Tsinghua University; Pinyan Lu, Shanghai University of Finance and Economics.

Boosting with Abstention. Corinna Cortes, ; Giulia DeSalvo*, ; Mehryar Mohri, Regret of h&m delivery code, Queueing Bandits. Subhashini Krishnasamy, The University of what is a system, Texas at h&m delivery Austin; Rajat Sen, The University of Texas at what market Austin; Ramesh Johari, ; Sanjay Shakkottai*, The University of Texas at Aus. Dale Schuurmans*, ; Martin Zinkevich, Google. Globally Optimal Training of code, Generalized Polynomial Neural Networks with Nonlinear Spectral Methods. Antoine Gautier*, Saarland University; Quynh Nguyen, Saarland University; Matthias Hein, Saarland University. Learning Volumetric 3D Object Reconstruction from tns earthing, Single-View with Projective Transformations. Xinchen Yan*, University of h&m delivery, Michigan; Jimei Yang, ; Ersin Yumer, Adobe Research; Yijie Guo, University of Michigan; Honglak Lee, University of Michigan. A Credit Assignment Compiler for goals for a student, Joint Prediction.

Kai-Wei Chang*, ; He He, University of h&m delivery, Maryland; Stephane Ross, Google; Hal III, ; John Langford, Accelerating Stochastic Composition Optimization. Reward Augmented Maximum Likelihood for islam rituals, Neural Structured Prediction. Mohammad Norouzi*, ; Dale Schuurmans, ; Samy Bengio, ; zhifeng Chen, ; Navdeep Jaitly, ; Mike Schuster, ; Yonghui Wu, Consistent Kernel Mean Estimation for Functions of Random Variables. Adam Scibior*, University of h&m delivery code, Cambridge; Carl-Johann Simon-Gabriel, MPI Tuebingen; Iliya Tolstikhin, ; Bernhard Schoelkopf, Towards Unifying Hamiltonian Monte Carlo and Capital Punishment, Slice Sampling. Yizhe Zhang*, Duke university; Xiangyu Wang, Duke University; Changyou Chen, ; Ricardo Henao, ; Kai Fan, Duke university; Lawrence Carin, Scalable Adaptive Stochastic Optimization Using Random Projections. Gabriel Krummenacher*, ETH Zurich; Brian Mcwilliams, Disney Research; Yannic Kilcher, ETH Zurich; Joachim Buhmann, ETH Zurich; Nicolai Meinshausen,

Variational Inference in Mixed Probabilistic Submodular Models. Josip Djolonga, ETH Zurich; Sebastian Tschiatschek*, ETH Zurich; Andreas Krause, Correlated-PCA: Principal Components' Analysis when Data and code, Noise are Correlated. Namrata Vaswani*, ; Han Guo, Iowa State University. The Multi-fidelity Multi-armed Bandit. Kirthevasan Kandasamy*, CMU; Gautam Dasarathy, Carnegie Mellon University; Barnabas Poczos, ; Jeff Schneider, CMU. Anchor-Free Correlated Topic Modeling: Identifiability and about Hitler, Algorithm. Kejun Huang*, University of Minnesota; Xiao Fu, University of h&m delivery, Minnesota; Nicholas Sidiropoulos, University of Minnesota.

Bootstrap Model Aggregation for Distributed Statistical Learning. JUN HAN, Dartmouth College; Qiang Liu*, A scalable end-to-end Gaussian process adapter for of Versailles, irregularly sampled time series classification. Steven Cheng-Xian Li*, UMass Amherst; Benjamin Marlin, A Bandit Framework for h&m delivery code, Strategic Regression. Yang Liu*, Harvard University; Yiling Chen, Architectural Complexity Measures of Recurrent Neural Networks. Saizheng Zhang*, University of list term goals for a student, Montreal; Yuhuai Wu, University of h&m delivery code, Toronto; Tong Che, IHES; Zhouhan Lin, University of of short goals for a student, Montreal; Roland Memisevic, University of Montreal; Ruslan Salakhutdinov, University of Toronto; Yoshua Bengio, U. H&m Delivery Code? Montreal. Statistical Inference for tns earthing, Cluster Trees.

Jisu Kim*, Carnegie Mellon University; Yen-Chi Chen, Carnegie Mellon University; Sivaraman Balakrishnan, Carnegie Mellon University; Alessandro Rinaldo, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University. Contextual-MDPs for h&m delivery code, PAC Reinforcement Learning with Rich Observations. Akshay Krishnamurthy*, ; Alekh Agarwal, Microsoft; John Langford, Improved Deep Metric Learning with Multi-class N-pair Loss Objective. Only H is islam rituals, left: Near-tight Episodic PAC RL. Christoph Dann*, Carnegie Mellon University; Emma Brunskill, Carnegie Mellon University Unsupervised Learning of h&m delivery code, Spoken Language with Visual Context. David Harwath*, MIT CSAIL; Antonio Torralba, MIT CSAIL; James Glass, MIT CSAIL.

Low-Rank Regression with Tensor Responses. Guillaume Rabusseau*, Aix-Marseille University; Hachem Kadri, PAC-Bayesian Theory Meets Bayesian Inference. Pascal Germain*, ; Francis Bach, ; Alexandre Lacoste, ; Simon Lacoste-Julien, INRIA. Data Poisoning Attacks on Factorization-Based Collaborative Filtering. Bo Li*, Vanderbilt University; Yining Wang, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; yevgeniy Vorobeychik, Vanderbilt University. Learned Region Sparsity and Diversity Also Predicts Visual Attention. Zijun Wei*, Stony Brook; Hossein Adeli, ; Minh Hoai, ; Gregory Zelinsky, ; Dimitris Samaras,

End-to-End Goal-Driven Web Navigation. Rodrigo Frassetto Nogueira*, New York University; Kyunghyun Cho, University of Montreal. Automated scalable segmentation of Punishment, neurons from multispectral images. Uygar Sumbul*, Columbia University; Douglas Roossien, University of h&m delivery, Michigan, Ann Arbor; Dawen Cai, University of goals for a, Michigan, Ann Arbor; John Cunningham, Columbia University; Liam Paninski, Privacy Odometers and Filters: Pay-as-you-Go Composition. Ryan Rogers*, University of h&m delivery, Pennsylvania; Salil Vadhan, Harvard University; Aaron Roth, ; Jonathan Robert Ullman, Minimax Estimation of what is a market system, Maximal Mean Discrepancy with Radial Kernels. Iliya Tolstikhin*, ; Bharath Sriperumbudur, ; Bernhard Schoelkopf, Adaptive optimal training of animal behavior. Ji Hyun Bak*, Princeton University; Jung Yoon Choi, ; Ilana Witten, ; Jonathan Pillow, Hierarchical Object Representation for Open-Ended Object Category Learning and h&m delivery code, Recognition.

Hamidreza Kasaei*, IEETA, University of Capital Punishment Is Barbaric, Aveiro. Relevant sparse codes with variational information bottleneck. Matthew Chalk*, IST Austria; Olivier Marre, Institut de la vision; Gasper Tkacik, Institute of Science and h&m delivery code, Technology Austria. Combinatorial Energy Learning for Image Segmentation. Jeremy Maitin-Shepard*, Google; Viren Jain, Google; Michal Januszewski, Google; Peter Li, ; Pieter Abbeel, Orthogonal Random Features. Felix Xinnan Yu*, ; Ananda Theertha Suresh, ; Krzysztof Choromanski, ; Dan Holtmann-Rice, ; Sanjiv Kumar, Google. Fast Active Set Methods for Online Spike Inference from Calcium Imaging.

Johannes Friedrich*, Columbia University; Liam Paninski, Diffusion-Convolutional Neural Networks. James Atwood*, UMass Amherst. Bayesian latent structure discovery from multi-neuron recordings. Scott Linderman*, ; Ryan Adams, ; Jonathan Pillow, A Probabilistic Programming Approach To Probabilistic Data Analysis. Feras Saad*, MIT; Vikash Mansinghka, MIT. A Non-parametric Learning Method for Punishment Is Barbaric Essay, Confidently Estimating Patient's Clinical State and Dynamics. William Hoiles*, University of California, Los ; Mihaela Van Der Schaar,

Inference by Reparameterization in code Neural Population Codes. RAJKUMAR VASUDEVA RAJU, Rice University; Xaq Pitkow*, Tensor Switching Networks. Chuan-Yung Tsai*, ; Andrew Saxe, ; David Cox, Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. Alain Durmus, Telecom ParisTech; Umut Simsekli*, ; Eric Moulines, Ecole Polytechnique; Roland Badeau, Telecom ParisTech; Gael Richard, Telecom ParisTech. Coordinate-wise Power Method. Qi Lei*, UT AUSTIN; Kai Zhong, UT AUSTIN; Inderjit Dhillon, Learning Influence Functions from islam rituals, Incomplete Observations.

Xinran He*, USC; Ke Xu, USC; David Kempe, USC; Yan Liu, Learning Structured Sparsity in Deep Neural Networks. Wei Wen*, University of h&m delivery, Pittsburgh; Chunpeng Wu, University of Pittsburgh; Yandan Wang, University of islam rituals, Pittsburgh; Yiran Chen, University of h&m delivery, Pittsburgh; Hai Li, University of goals for a, Pittsburg. Sample Complexity of h&m delivery, Automated Mechanism Design. Nina Balcan, ; Tuomas Sandholm, Carnegie Mellon University; Ellen Vitercik*, Carnegie Mellon University. Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. SANGHAMITRA DUTTA*, Carnegie Mellon University; Viveck Cadambe, Pennsylvania State University; Pulkit Grover, Carnegie Mellon University. Umut Guclu*, Radboud University; Jordy Thielen, Radboud University; Michael Hanke, Otto-von-Guericke University Magdeburg; Marcel Van Gerven, Radboud University. Learning Transferrable Representations for tns earthing, Unsupervised Domain Adaptation. Ozan Sener*, Cornell University; Hyun Oh Song, Google Research; Ashutosh Saxena, Brain of h&m delivery code, Things; Silvio Savarese, Stanford University.

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles. Stefan Lee*, Indiana University; Senthil Purushwalkam, Carnegie Mellon; Michael Cogswell, Virginia Tech; Viresh Ranjan, Virginia Tech; David Crandall, Indiana University; Dhruv Batra, Active Learning from Imperfect Labelers. Songbai Yan*, University of what system, California, San Diego; Kamalika Chaudhuri, University of h&m delivery code, California, San Diego; Tara Javidi, University of California, San Diego. Learning to Communicate with Deep Multi-Agent Reinforcement Learning. Jakob Foerster*, University of Oxford; Yannis Assael, University of Punishment Is Barbaric Essay, Oxford; Nando de Freitas, University of Oxford; Shimon Whiteson,

Value Iteration Networks. Aviv Tamar*, ; Sergey Levine, ; Pieter Abbeel, ; Yi Wu, UC Berkeley; Garrett Thomas, UC Berkeley. Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering. Dogyoon Song*, MIT; Christina Lee, MIT; Yihua Li, MIT; Devavrat Shah, On the h&m delivery code, Recursive Teaching Dimension of VC Classes. Bo Tang*, University of Essay about Hitler of Versailles, Oxford; Xi Chen, Columbia University; Yu Cheng, U of h&m delivery, Southern California. InfoGAN: Interpretable Representation Learning by islam rituals Information Maximizing Generative Adversarial Nets. Xi Chen*, UC Berkeley; OpenAI; Yan Duan, UC Berkeley; Rein Houthooft, Ghent University - iMinds; UC Berkeley; OpenAI; John Schulman, OpenAI; Ilya Sutskever, ; Pieter Abbeel,

Hardness of h&m delivery, Online Sleeping Combinatorial Optimization Problems. Satyen Kale*, ; Chansoo Lee, ; David Pal, Mixed Linear Regression with Multiple Components. Kai Zhong*, UT AUSTIN; Prateek Jain, Microsoft Research; Inderjit Dhillon, Sequential Neural Models with Stochastic Layers. Marco Fraccaro*, DTU; Soren Sonderby, KU; Ulrich Paquet, ; Ole Winther, DTU. Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences.

Hongseok Namkoong*, Stanford University; John Duchi, Minimizing Quadratic Functions in list term for a student Constant Time. Kohei Hayashi*, AIST; Yuichi Yoshida, NII. Improved Techniques for Training GANs. Tim Salimans*, ; Ian Goodfellow, OpenAI; Wojciech Zaremba, OpenAI; Vicki Cheung, OpenAI; Alec Radford, OpenAI; Xi Chen, UC Berkeley; OpenAI. DeepMath - Deep Sequence Models for Premise Selection. Geoffrey Irving*, ; Christian Szegedy, ; Alexander Alemi, Google; Francois Chollet, ; Josef Urban, Czech Technical University in h&m delivery code Prague. Learning Multiagent Communication with Backpropagation. Sainbayar Sukhbaatar, NYU; Arthur Szlam, ; Rob Fergus*, New York University Toward Deeper Understanding of Neural Networks: The Power of Human Cloning Essay, Initialization and a Dual View on code, Expressivity. Amit Daniely*, ; Roy Frostig, Stanford University; Yoram Singer, Google.

Learning the Capital, Number of h&m delivery, Neurons in Trials Deep Networks. Jose Alvarez*, NICTA; Mathieu Salzmann, EPFL. Finding significant combinations of features in code the presence of islam rituals, categorical covariates. Laetitia Papaxanthos*, ETH Zurich; Felipe Llinares, ETH Zurich; Dean Bodenham, ETH Zurich; Karsten Borgwardt, Examples are not Enough, Learn to Criticize! Model Criticism for Interpretable Machine Learning. Been Kim*, ; Rajiv Khanna, UT Austin; Sanmi Koyejo, UIUC. Optimistic Bandit Convex Optimization. Scott Yang*, New York University; Mehryar Mohri,

Safe Policy Improvement by Minimizing Robust Baseline Regret. Mohamad Ghavamzadeh*, ; Marek Petrik, ; Yinlam Chow, Stanford University. Graphons, mergeons, and h&m delivery code, so on! Justin Eldridge*, The Ohio State University; Mikhail Belkin, ; Yusu Wang, The Ohio State University. Hierarchical Clustering via Spreading Metrics. Aurko Roy*, Georgia Tech; Sebastian Pokutta, GeorgiaTech. Learning Bayesian networks with ancestral constraints. Eunice Yuh-Jie Chen*, UCLA; Yujia Shen, ; Arthur Choi, ; Adnan Darwiche, Pruning Random Forests for islam rituals, Prediction on h&m delivery code, a Budget. Feng Nan*, Boston University; Joseph Wang, Boston University; Venkatesh Saligrama,

Clustering with Bregman Divergences: an Essay about Hitler and the Asymptotic Analysis. Chaoyue Liu*, The Ohio State University; Mikhail Belkin, Variational Autoencoder for h&m delivery code, Deep Learning of Images, Labels and Is Barbaric, Captions. Yunchen Pu*, Duke University; Zhe Gan, Duke; Ricardo Henao, ; Xin Yuan, Bell Labs; chunyuan Li, Duke; Andrew Stevens, Duke University; Lawrence Carin, Encode, Review, and h&m delivery, Decode: Reviewer Module for Human Cloning Trials, Caption Generation. Zhilin Yang*, Carnegie Mellon University; Ye Yuan, Carnegie Mellon University; Yuexin Wu, Carnegie Mellon University; William Cohen, Carnegie Mellon University; Ruslan Salakhutdinov, University of h&m delivery, Toronto.

Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. Qiang Liu*, ; Dilin Wang, Dartmouth College. A Bio-inspired Redundant Sensing Architecture. Anh Tuan Nguyen*, University of Minnesota; Jian Xu, University of Hitler, Minnesota; Zhi Yang, University of code, Minnesota. Contextual semibandits via supervised learning oracles. Akshay Krishnamurthy*, ; Alekh Agarwal, Microsoft; Miro Dudik, Blind Attacks on market system, Machine Learners. Alex Beatson*, Princeton University; Zhaoran Wang, Princeton University; Han Liu, Universal Correspondence Network. Christopher Choy*, Stanford University; Manmohan Chandraker, NEC Labs America; JunYoung Gwak, Stanford University; Silvio Savarese, Stanford University.

Satisfying Real-world Goals with Dataset Constraints. Gabriel Goh*, UC Davis; Andy Cotter, ; Maya Gupta, ; Michael Friedlander, UC Davis. Deep Learning for h&m delivery code, Predicting Human Strategic Behavior. Jason Hartford*, University of Essay about Hitler Effects of Treaty, British Columbia; Kevin Leyton-Brown, ; James Wright, University of British Columbia. Phased Exploration with Greedy Exploitation in h&m delivery Stochastic Combinatorial Partial Monitoring Games. Sougata Chaudhuri*, University of tns earthing, Michigan ; Ambuj Tewari, University of h&m delivery, Michigan. Eliciting and Essay and the of Versailles, Aggregating Categorical Data. Yiling Chen, ; Rafael Frongillo, ; Chien-Ju Ho*,

Measuring the h&m delivery code, reliability of is a market system, MCMC inference with Bidirectional Monte Carlo. Roger Grosse, ; Siddharth Ancha, University of h&m delivery, Toronto; Daniel Roy*, Breaking the system, Bandwidth Barrier: Geometrical Adaptive Entropy Estimation. Weihao Gao, UIUC; Sewoong Oh*, ; Pramod Viswanath, UIUC. Selective inference for h&m delivery code, group-sparse linear models. Fan Yang, University of Chicago; Rina Foygel Barber*, ; Prateek Jain, Microsoft Research; John Lafferty, Graph Clustering: Block-models and Capital Essay, model free results. Yali Wan*, University of h&m delivery, Washington; Marina Meila, University of Washington. Maximizing Influence in for a an Ising Network: A Mean-Field Optimal Solution. Christopher Lynn*, University of h&m delivery code, Pennsylvania; Dan Lee , University of Pennsylvania.

Hypothesis Testing in Capital Punishment Is Barbaric Essay Unsupervised Domain Adaptation with Applications in Neuroscience. Hao Zhou, University of Wisconsin Madiso; Vamsi Ithapu*, University of code, Wisconsin Madison; Sathya Ravi, University of Wisconsin Madiso; Vikas Singh, UW Madison; Grace Wahba, University of tns earthing, Wisconsin Madison; Sterling Johnson, University of Wisconsin Madison. Geometric Dirichlet Means Algorithm for Topic Inference. Mikhail Yurochkin*, University of h&m delivery, Michigan; Long Nguyen, Structured Prediction Theory Based on what market, Factor Graph Complexity. Corinna Cortes, ; Vitaly Kuznetsov*, Courant Institute; Mehryar Mohri, ; Scott Yang, New York University. Improved Dropout for h&m delivery code, Shallow and Deep Learning. Zhe Li, The University of Iowa; Boqing Gong, University of Trials, Central Florida; Tianbao Yang*, University of Iowa. Constraints Based Convex Belief Propagation. Yaniv Tenzer*, The Hebrew University; Alexander Schwing, ; Kevin Gimpel, ; Tamir Hazan,

Error Analysis of h&m delivery code, Generalized Nystrom Kernel Regression. Hong Chen, University of Essay about Hitler, Texas; Haifeng Xia, Huazhong Agricultural University; Heng Huang*, University of Texas Arlington. A Probabilistic Framework for h&m delivery, Deep Learning. Ankit Patel, Baylor College of Medicine; Rice University; Tan Nguyen*, Rice University; Richard Baraniuk, General Tensor Spectral Co-clustering for Higher-Order Data. Tao Wu*, Purdue University; Austin Benson, Stanford University; David Gleich,

Cyclades: Conflict-free Asynchronous Machine Learning. Xinghao Pan*, UC Berkeley; Stephen Tu, UC Berkeley; Maximilian Lam, UC Berkeley; Dimitris Papailiopoulos, ; Ce Zhang, Stanford; Michael Jordan, ; Kannan Ramchandran, ; Christopher Re, ; Ben Recht, Single Pass PCA of Capital, Matrix Products. Shanshan Wu*, UT Austin; Srinadh Bhojanapalli, TTI Chicago; Sujay Sanghavi, ; Alexandros G. Dimakis, Stochastic Variational Deep Kernel Learning. Andrew Wilson*, Carnegie Mellon University; Zhiting Hu, Carnegie Mellon University; Ruslan Salakhutdinov, University of Toronto; Eric Xing, Carnegie Mellon University. Interaction Screening: Efficient and Sample-Optimal Learning of h&m delivery, Ising Models. Marc Vuffray*, Los Alamos National Laboratory; Sidhant Misra, Los Alamos National Laboratory; Andrey Lokhov, Los Alamos National Laboratory; Misha Chertkov, Los Alamos National Laboratory. Long-term Causal Effects via Behavioral Game Theory.

Panos Toulis*, University of islam rituals, Chicago; David Parkes, Harvard University. Measuring Neural Net Robustness with Constraints. Osbert Bastani*, Stanford University; Yani Ioannou, University of h&m delivery, Cambridge; Leonidas Lampropoulos, University of Human, Pennsylvania; Dimitrios Vytiniotis, Microsoft Research; Aditya Nori, Microsoft Research; Antonio Criminisi, Reshaped Wirtinger Flow for h&m delivery, Solving Quadratic Systems of Capital Is Barbaric Essay, Equations. Huishuai Zhang*, Syracuse University; Yingbin Liang, Syracuse University. Nearly Isometric Embedding by h&m delivery Relaxation.

James McQueen*, University of islam rituals, Washington; Marina Meila, University of Washington; Dominique Joncas, Google. Probabilistic Inference with Generating Functions for Poisson Latent Variable Models. Kevin Winner*, UMass CICS; Daniel Sheldon, Causal meets Submodular: Subset Selection with Directed Information. Yuxun Zhou*, UC Berkeley; Costas Spanos,

Depth from h&m delivery, a Single Image by of short term goals for a student Harmonizing Overcomplete Local Network Predictions. Ayan Chakrabarti*, ; Jingyu Shao, UCLA; Greg Shakhnarovich, Deep Neural Networks with Inexact Matching for h&m delivery code, Person Re-Identification. Arulkumar Subramaniam, IIT Madras; Moitreya Chatterjee*, IIT Madras; Anurag Mittal, IIT Madras. Global Analysis of Expectation Maximization for Mixtures of tns earthing, Two Gaussians.

Ji Xu, Columbia university; Daniel Hsu*, ; Arian Maleki, Columbia University. Estimating the code, class prior and posterior from Human Trials, noisy positives and h&m delivery code, unlabeled data. Shanatnu Jain*, Indiana University; Martha White, ; Predrag Radivojac, Kronecker Determinantal Point Processes. Zelda Mariet*, MIT; Suvrit Sra, MIT. Finite Sample Prediction and of short term goals, Recovery Bounds for h&m delivery code, Ordinal Embedding. Lalit Jain*, University of list of short term goals student, Wisconsin-Madison; Kevin Jamieson, UC Berkeley; Robert Nowak, University of code, Wisconsin Madison. Feature-distributed sparse regression: a screen-and-clean approach.

Jiyan Yang*, Stanford University; Michael Mahoney, ; Michael Saunders, Stanford University; Yuekai Sun, University of Michigan. Learning Bound for Human Essay, Parameter Transfer Learning. Wataru Kumagai*, Kanagawa University. Learning under uncertainty: a comparison between R-W and Bayesian approach. He Huang*, LIBR; Martin Paulus, LIBR. Bi-Objective Online Matching and Submodular Allocations. Hossein Esfandiari*, University of h&m delivery, Maryland; Nitish Korula, Google Research; Vahab Mirrokni, Google. Quantized Random Projections and Non-Linear Estimation of tns earthing, Cosine Similarity. Ping Li, ; Michael Mitzenmacher, Harvard University; Martin Slawski*, The non-convex Burer-Monteiro approach works on smooth semidefinite programs. Nicolas Boumal, ; Vlad Voroninski*, MIT; Afonso Bandeira,

Dimensionality Reduction of Massive Sparse Datasets Using Coresets. Dan Feldman, ; Mikhail Volkov*, MIT; Daniela Rus, MIT. Using Social Dynamics to h&m delivery, Make Individual Predictions: Variational Inference with Stochastic Kinetic Model. Zhen Xu*, SUNY at tns earthing Buffalo; Wen Dong, ; Sargur Srihari, Supervised learning through the lens of h&m delivery, compression. Ofir David*, Technion - Israel institute of technology; Shay Moran, Technion - Israel institue of Technology; Amir Yehudayoff, Technion - Israel institue of term goals student, Technology.

Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data. Xinghua Lou*, Vicarious FPC Inc; Ken Kansky, ; Wolfgang Lehrach, ; CC Laan, ; Bhaskara Marthi, ; D. Scott Phoenix, ; Dileep George, Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections. Xiao-Jiao Mao, Nanjing University; Chunhua Shen*, ; Yu-Bin Yang, Object based Scene Representations using Fisher Scores of h&m delivery, Local Subspace Projections. Mandar Dixit*, UC San Diego; Nuno Vasconcelos, Active Learning with Oracle Epiphany. Tzu-Kuo Huang, Microsoft Research; Lihong Li, Microsoft Research; Ara Vartanian, University of Wisconsin-Madison; Saleema Amershi, Microsoft; Xiaojin Zhu*, Statistical Inference for list term goals student, Pairwise Graphical Models Using Score Matching. Ming Yu*, The University of Chicago; Mladen Kolar, ; Varun Gupta, University of h&m delivery code, Chicago. Improved Error Bounds for tns earthing, Tree Representations of h&m delivery, Metric Spaces.

Samir Chowdhury*, The Ohio State University; Facundo Memoli, ; Zane Smith, Can Peripheral Representations Improve Clutter Metrics on term for a, Complex Scenes? Arturo Deza*, UCSB; Miguel Eckstein, UCSB. On Multiplicative Integration with Recurrent Neural Networks. Yuhuai Wu*, University of code, Toronto; Saizheng Zhang, University of tns earthing, Montreal; ying Zhang, University of h&m delivery code, Montreal; Yoshua Bengio, U. Montreal; Ruslan Salakhutdinov, University of Toronto. Learning HMMs with Nonparametric Emissions via Spectral Decompositions of tns earthing, Continuous Matrices. Kirthevasan Kandasamy*, CMU; Maruan Al-Shedivat, CMU; Eric Xing, Carnegie Mellon University.

Regret Bounds for h&m delivery, Non-decomposable Metrics with Missing Labels. Nagarajan Natarajan*, Microsoft Research Bangalore; Prateek Jain, Microsoft Research. Robust k-means: a Theoretical Revisit. ALEXANDROS GEORGOGIANNIS*, TECHNICAL UNIVERSITY OF CRETE. Bayesian optimization for automated model selection. Gustavo Malkomes, Washington University; Charles Schaff, Washington University in St. Is Barbaric Essay? Louis; Roman Garnett*, A Probabilistic Model of h&m delivery code, Social Decision Making based on Reward Maximization. Koosha Khalvati*, University of Washington; Seongmin Park, Cognitive Neuroscience Center; Jean-Claude Dreher, Centre de Neurosciences Cognitives; Rajesh Rao, University of islam rituals, Washington. Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition.

Ahmed Alaa*, UCLA; Mihaela Van Der Schaar, Fast and Flexible Monotonic Functions with Ensembles of code, Lattices. Mahdi Fard, ; Kevin Canini, ; Andy Cotter, ; Jan Pfeifer, Google; Maya Gupta*, Conditional Generative Moment-Matching Networks. Yong Ren, Tsinghua University; Jun Zhu*, ; Jialian Li, Tsinghua University; Yucen Luo, Stochastic Gradient MCMC with Stale Gradients. Changyou Chen*, ; Nan Ding, Google; chunyuan Li, Duke; Yizhe Zhang, Duke university; Lawrence Carin, Composing graphical models with neural networks for islam rituals, structured representations and fast inference. Matthew Johnson, ; David Duvenaud*, ; Alex Wiltschko, Harvard University and Twitter; Ryan Adams, ; Sandeep Datta, Harvard Medical School. Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling. Nina Balcan, ; Hongyang Zhang*, CMU.

Combinatorial semi-bandit with known covariance. Remy Degenne*, Universite Paris Diderot; Vianney Perchet, Matrix Completion has No Spurious Local Minimum. Rong Ge, ; Jason Lee, UC Berkeley; Tengyu Ma*, Princeton University. The Multiscale Laplacian Graph Kernel.

Risi Kondor*, ; Horace Pan, UChicago. Adaptive Averaging in Accelerated Descent Dynamics. Walid Krichene*, UC Berkeley; Alexandre Bayen, UC Berkeley; Peter Bartlett, Sub-sampled Newton Methods with Non-uniform Sampling. Peng Xu*, Stanford University; Jiyan Yang, Stanford University; Farbod Roosta-Khorasani, University of h&m delivery code, California Berkeley; Christopher Re, ; Michael Mahoney, Stochastic Gradient Geodesic MCMC Methods. Chang Liu*, Tsinghua University; Jun Zhu, ; Yang Song, Stanford University. Variational Bayes on Monte Carlo Steroids. Aditya Grover*, Stanford University; Stefano Ermon,

Showing versus doing: Teaching by demonstration. Mark Ho*, Brown University; Michael L. Capital Is Barbaric Essay? Littman, ; James MacGlashan, Brown University; Fiery Cushman, Harvard University; Joe Austerweil, Combining Fully Convolutional and code, Recurrent Neural Networks for is a market, 3D Biomedical Image Segmentation. Jianxu Chen*, University of code, Notre Dame; Lin Yang, University of and the Effects, Notre Dame; Yizhe Zhang, University of h&m delivery code, Notre Dame; Mark Alber, University of Notre Dame; Danny Chen, University of islam rituals, Notre Dame. Maximization of Approximately Submodular Functions. Thibaut Horel*, Harvard University; Yaron Singer, A Comprehensive Linear Speedup Analysis for code, Asynchronous Stochastic Parallel Optimization from islam rituals, Zeroth-Order to First-Order. Xiangru Lian, University of code, Rochester; Huan Zhang, ; Cho-Jui Hsieh, ; Yijun Huang, ; Ji Liu*,

Learning Infinite RBMs with Frank-Wolfe. Wei Ping*, UC Irvine; Qiang Liu, ; Alexander Ihler, Estimating the Size of a Large Network and tns earthing, its Communities from code, a Random Sample. Lin Chen*, Yale University; Amin Karbasi, ; Forrest Crawford, Yale University. Learning Sensor Multiplexing Design through Back-propagation. On Robustness of Kernel Clustering. Bowei Yan*, University of tns earthing, Texas at h&m delivery code Austin; Purnamrita Sarkar, U.C. Berkeley.

High resolution neural connectivity from incomplete tracing data using nonnegative spline regression. Kameron Harris*, University of Human Cloning Essay, Washington; Stefan Mihalas, Allen Institute for code, Brain Science; Eric Shea-Brown, University of Washington. MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild. Gregory Rogez*, Inria; Cordelia Schmid, A New Liftable Class for First-Order Probabilistic Inference. Seyed Mehran Kazemi*, UBC; Angelika Kimmig, KU Leuven; Guy Van den Broeck, ; David Poole, UBC. The Parallel Knowledge Gradient Method for Batch Bayesian Optimization. Jian Wu*, Cornell University; Peter I. Islam Rituals? Frazier,

Improved Regret Bounds for h&m delivery code, Oracle-Based Adversarial Contextual Bandits. Vasilis Syrgkanis*, ; Haipeng Luo, Princeton University; Akshay Krishnamurthy, ; Robert Schapire, Consistent Estimation of list term for a student, Functions of code, Data Missing Non-Monotonically and market, Not at Random. Optimistic Gittins Indices. Eli Gutin*, Massachusetts Institute of h&m delivery code, Tec; Vivek Farias, Finite-Dimensional BFRY Priors and Variational Bayesian Inference for islam rituals, Power Law Models. Juho Lee*, POSTECH; Lancelot James, HKUST; Seungjin Choi, POSTECH.

Launch and Iterate: Reducing Prediction Churn. Mahdi Fard, ; Quentin Cormier, Google; Kevin Canini, ; Maya Gupta*, “Congruent” and code, “Opposite” Neurons: Sisters for Multisensory Integration and about of Treaty of Versailles, Segregation. Wen-Hao Zhang*, Institute of code, Neuroscience, Chinese Academy of Human Cloning, Sciences; He Wang, HKUST; K. Y. H&m Delivery Code? Michael Wong, HKUST; Si Wu, Learning shape correspondence with anisotropic convolutional neural networks. Davide Boscaini*, University of Lugano; Jonathan Masci, ; Emanuele Rodola, University of Hitler and the, Lugano; Michael Bronstein, University of h&m delivery code, Lugano. Pairwise Choice Markov Chains.

Stephen Ragain*, Stanford University; Johan Ugander, NESTT: A Nonconvex Primal-Dual Splitting Method for Human Essay, Distributed and Stochastic Optimization. Davood Hajinezhad*, Iowa State University; Mingyi Hong, ; Tuo Zhao, Johns Hopkins University; Zhaoran Wang, Princeton University. Clustering with Same-Cluster Queries. Hassan Ashtiani, University of h&m delivery, Waterloo; Shrinu Kushagra*, University of system, Waterloo; Shai Ben-David, U. Waterloo. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models.

S. M. Ali Eslami*, Google DeepMind; Nicolas Heess, ; Theophane Weber, ; Yuval Tassa, Google DeepMind; David Szepesvari, Google DeepMind; Koray Kavukcuoglu, Google DeepMind; Geoffrey Hinton, Google. Parameter Learning for h&m delivery code, Log-supermodular Distributions. Tatiana Shpakova*, Inria - ENS Paris; Francis Bach, Deconvolving Feedback Loops in Recommender Systems. Ayan Sinha*, Purdue; David Gleich, ; Karthik Ramani, Purdue University. Structured Matrix Recovery via the Trials Essay, Generalized Dantzig Selector. Sheng Chen*, University of h&m delivery code, Minnesota; Arindam Banerjee,

Confusions over Capital Punishment Essay, Time: An Interpretable Bayesian Model to code, Characterize Trends in Decision Making. Himabindu Lakkaraju*, Stanford University; Jure Leskovec, Automatic Neuron Detection in of short term goals Calcium Imaging Data Using Convolutional Networks. Noah Apthorpe*, Princeton University; Alexander Riordan, Princeton University; Robert Aguilar, Princeton University; Jan Homann, Princeton University; Yi Gu, Princeton University; David Tank, Princeton University; H. H&m Delivery? Sebastian Seung, Princeton University. Designing smoothing functions for improved worst-case competitive ratio in online optimization. Reza Eghbali*, University of Cloning Essay, washington; Maryam Fazel, University of Washington. Convergence guarantees for code, kernel-based quadrature rules in misspecified settings. Motonobu Kanagawa*, ; Bharath Sriperumbudur, ; Kenji Fukumizu, Unsupervised Learning from Noisy Networks with Applications to Hi-C Data. Bo Wang*, Stanford University; Junjie Zhu, Stanford University; Armin Pourshafeie, Stanford University.

A non-generative framework and convex relaxations for unsupervised learning. Elad Hazan, ; Tengyu Ma*, Princeton University. Equality of Opportunity in what market system Supervised Learning. Moritz Hardt*, ; Eric Price, ; Nathan Srebro, Scaled Least Squares Estimator for h&m delivery, GLMs in islam rituals Large-Scale Problems. Murat Erdogdu*, Stanford University; Lee Dicker, ; Mohsen Bayati,

Interpretable Nonlinear Dynamic Modeling of code, Neural Trajectories. Yuan Zhao*, Stony Brook University; Il Memming Park, Search Improves Label for Active Learning. Alina Beygelzimer, Yahoo Inc; Daniel Hsu, ; John Langford, ; Chicheng Zhang*, UCSD. Higher-Order Factorization Machines.

Mathieu Blondel*, NTT; Akinori Fujino, NTT; Naonori Ueda, ; Masakazu Ishihata, Hokkaido University. Exponential expressivity in islam rituals deep neural networks through transient chaos. Ben Poole*, Stanford University; Subhaneil Lahiri, Stanford University; Maithra Raghu, Cornell University; Jascha Sohl-Dickstein, ; Surya Ganguli, Stanford. Split LBI: An Iterative Regularization Path with Structural Sparsity. Chendi Huang, Peking University; Xinwei Sun, ; Jiechao Xiong, Peking University; Yuan Yao*, An equivalence between high dimensional Bayes optimal inference and code, M-estimation. Madhu Advani*, Stanford University; Surya Ganguli, Stanford. Synthesizing the preferred inputs for neurons in islam rituals neural networks via deep generator networks. Anh Nguyen*, University of code, Wyoming; Alexey Dosovitskiy, ; Jason Yosinski, Cornell; Thomas Brox, University of Freiburg; Jeff Clune,

Deep Submodular Functions. Brian Dolhansky*, University of goals for a, Washington; Jeff Bilmes, University of Washington, Seattle. Discriminative Gaifman Models. Leveraging Sparsity for code, Efficient Submodular Data Summarization. Erik Lindgren*, University of Human Cloning, Texas at Austin; Shanshan Wu, UT Austin; Alexandros G. H&m Delivery Code? Dimakis, Local Minimax Complexity of Stochastic Convex Optimization. Sabyasachi Chatterjee, University of what is a, Chicago; John Duchi, ; John Lafferty, ; Yuancheng Zhu*, University of Chicago. Stochastic Optimization for code, Large-scale Optimal Transport.

Aude Genevay*, Universite Paris Dauphine; Marco Cuturi, ; Gabriel Peyre, ; Francis Bach, On Mixtures of Markov Chains. Rishi Gupta*, Stanford; Ravi Kumar, ; Sergei Vassilvitskii, Google. Linear Contextual Bandits with Knapsacks. Shipra Agrawal*, ; Nikhil Devanur, Microsoft Research. Reconstructing Parameters of Spreading Models from and the of Treaty, Partial Observations. Andrey Lokhov*, Los Alamos National Laboratory. Spatiotemporal Residual Networksfor Video Action Recognition. Christoph Feichtenhofer*, Graz University of Technology; Axel Pinz, Graz University of Technology; Richard Wildes, York University Toronto.

Path-Normalized Optimization of code, Recurrent Neural Networks with ReLU Activations. Behnam Neyshabur*, TTI-Chicago; Yuhuai Wu, University of Toronto; Ruslan Salakhutdinov, University of Capital Is Barbaric Essay, Toronto; Nathan Srebro, Strategic Attentive Writer for Learning Macro-Actions. Alexander Vezhnevets*, Google DeepMind; Volodymyr Mnih, ; Simon Osindero, Google DeepMind; Alex Graves, ; Oriol Vinyals, ; John Agapiou, ; Koray Kavukcuoglu, Google DeepMind. The Limits of Learning with Missing Data. Brian Bullins*, Princeton University; Elad Hazan, ; Tomer Koren, Technion---Israel Inst. of code, Technology. RETAIN: Interpretable Predictive Model in tns earthing Healthcare using Reverse Time Attention Mechanism. Edward Choi*, Georgia Institute of Technolog; Mohammad Taha Bahadori, Gatech; Jimeng Sun, Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers. Yu-Xiang Wang*, Carnegie Mellon University; Veeranjaneyulu Sadhanala, Carnegie Mellon University; Ryan Tibshirani,

Community Detection on Evolving Graphs. Stefano Leonardi*, Sapienza University of Rome; Aris Anagnostopoulos, Sapienza University of h&m delivery code, Rome; Jakub Lacki, Sapienza University of Trials, Rome; Silvio Lattanzi, Google; Mohammad Mahdian, Google Research, New York. Online and Differentially-Private Tensor Decomposition. Yining Wang*, Carnegie Mellon University; Anima Anandkumar, UC Irvine. Dimension-Free Iteration Complexity of h&m delivery code, Finite Sum Optimization Problems. Yossi Arjevani*, Weizmann Institute of Science; Ohad Shamir, Weizmann Institute of list of short goals student, Science.

Towards Conceptual Compression. Karol Gregor*, ; Frederic Besse, Google DeepMind; Danilo Jimenez Rezende, ; Ivo Danihelka, ; Daan Wierstra, Google DeepMind. Exact Recovery of Hard Thresholding Pursuit. Xiaotong Yuan*, Nanjing University of code, Informat; Ping Li, ; Tong Zhang, Data Programming: Creating Large Training Sets, Quickly. Alexander Ratner*, Stanford University; Christopher De Sa, Stanford University; Sen Wu, Stanford University; Daniel Selsam, Stanford; Christopher Re, Stanford University. Generalization of tns earthing, ERM in h&m delivery code Stochastic Convex Optimization: The Dimension Strikes Back. Dynamic matrix recovery from system, incomplete observations under an h&m delivery code exact low-rank constraint.

Liangbei Xu*, Gatech; Mark Davenport, Fast Distributed Submodular Cover: Public-Private Data Summarization. Baharan Mirzasoleiman*, ETH Zurich; Morteza Zadimoghaddam, ; Amin Karbasi, Estimating Nonlinear Neural Response Functions using GP Priors and list term, Kronecker Methods. Cristina Savin*, IST Austria; Gasper Tkacik, Institute of Science and Technology Austria. Lifelong Learning with Weighted Majority Votes. Anastasia Pentina*, IST Austria; Ruth Urner, MPI Tuebingen. Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. Jack Rae*, Google DeepMind; Jonathan Hunt, ; Ivo Danihelka, ; Tim Harley, Google DeepMind; Andrew Senior, ; Greg Wayne, ; Alex Graves, ; Timothy Lillicrap, Google DeepMind. Matching Networks for code, One Shot Learning.

Oriol Vinyals*, ; Charles Blundell, DeepMind; Timothy Lillicrap, Google DeepMind; Koray Kavukcuoglu, Google DeepMind; Daan Wierstra, Google DeepMind. Tight Complexity Bounds for Optimizing Composite Objectives. Blake Woodworth*, Toyota Technological Institute; Nathan Srebro, Graphical Time Warping for of short term goals for a student, Joint Alignment of h&m delivery code, Multiple Curves. Yizhi Wang, Virginia Tech; David Miller, The Pennsylvania State University; Kira Poskanzer, University of California, San Francisco; Yue Wang, Virginia Tech; Lin Tian, The University of California, Davis; Guoqiang Yu*, Unsupervised Risk Estimation Using Only Conditional Independence Structure. Jacob Steinhardt*, Stanford University; Percy Liang, MetaGrad: Multiple Learning Rates in Online Learning. Tim Van Erven*, ; Wouter M. What Is A System? Koolen,

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and code, Intrinsic Motivation. Tejas Kulkarni, MIT; Karthik Narasimhan*, MIT; Ardavan Saeedi, MIT; Joshua Tenenbaum, High Dimensional Structured Superposition Models. Qilong Gu*, University of what is a market, Minnesota; Arindam Banerjee, Joint quantile regression in vector-valued RKHSs. Maxime Sangnier*, LTCI, CNRS, Telecom ParisTech; Olivier Fercoq, ; Florence d’Alche-Buc,

The Forget-me-not Process. Kieran Milan, Google DeepMind; Joel Veness*, ; James Kirkpatrick, Google DeepMind; Michael Bowling, ; Anna Koop, University of code, Alberta; Demis Hassabis, Wasserstein Training of list of short goals for a, Restricted Boltzmann Machines. Gregoire Montavon*, ; Klaus-Robert Muller, ; Marco Cuturi, Communication-Optimal Distributed Clustering.

Jiecao Chen, Indiana University Bloomington; He Sun*, The University of Bristol; David Woodruff, ; Qin Zhang, Probing the Compositionality of h&m delivery code, Intuitive Functions. Eric Schulz*, University College London; Joshua Tenenbaum, ; David Duvenaud, ; Maarten Speekenbrink, University College London; Sam Gershman, Ladder Variational Autoencoders. Casper Kaae Sonderby*, University of list term goals student, Copenhagen; Tapani Raiko, ; Lars Maaloe, Technical University of Denmark; Soren Sonderby, KU; Ole Winther, Technical University of h&m delivery code, Denmark. The Multiple Quantile Graphical Model. Alnur Ali*, Carnegie Mellon University; Zico Kolter, ; Ryan Tibshirani, Threshold Learning for list term goals for a student, Optimal Decision Making. Nathan Lepora*, University of Bristol. Unsupervised Feature Extraction by h&m delivery Time-Contrastive Learning and Nonlinear ICA.

Aapo Hyvarinen*, ; Hiroshi Morioka, University of list term goals for a, Helsinki. Can Active Memory Replace Attention? Lukasz Kaiser*, ; Samy Bengio, Minimax Optimal Alternating Minimization for code, Kernel Nonparametric Tensor Learning. Taiji Suzuki*, ; Heishiro Kanagawa, ; Hayato Kobayashi, ; Nobuyuki Shimizu, ; Yukihiro Tagami, Thomas Laurent*, Loyola Marymount University; James Von Brecht, CSULB; Xavier Bresson, ; Arthur Szlam, Learning Sparse Gaussian Graphical Models with Overlapping Blocks. Mohammad Javad Hosseini*, University of Washington; Su-In Lee,

Yggdrasil: An Optimized System for Training Deep Decision Trees at Capital Punishment Scale. Firas Abuzaid*, MIT; Joseph Bradley, Databricks; Feynman Liang, Cambridge University Engineering Department; Andrew Feng, Yahoo!; Lee Yang, Yahoo!; Matei Zaharia, MIT; Ameet Talwalkar, Average-case hardness of h&m delivery, RIP certification. Tengyao Wang, University of islam rituals, Cambridge; Quentin Berthet*, ; Yaniv Plan, University of h&m delivery, British Columbia. Forward models at Purkinje synapses facilitate cerebellar anticipatory control. Ivan Herreros-Alonso*, Universitat Pompeu Fabra; Xerxes Arsiwalla, ; Paul Verschure, Convolutional Neural Networks on Human Cloning Trials Essay, Graphs with Fast Localized Spectral Filtering. Michael Defferrard*, EPFL; Xavier Bresson, ; pierre Vandergheynst, EPFL.

Deep Unsupervised Exemplar Learning. MIGUEL BAUTISTA*, HEIDELBERG UNIVERSITY; Artsiom Sanakoyeu, Heidelberg University; Ekaterina Tikhoncheva, Heidelberg University; Bjorn Ommer, Large-Scale Price Optimization via Network Flow. Shinji Ito*, NEC Coorporation; Ryohei Fujimaki, Online Pricing with Strategic and h&m delivery, Patient Buyers. Michal Feldman, TAU; Tomer Koren, Technion---Israel Inst. of Capital Punishment, Technology; Roi Livni*, Huji; Yishay Mansour, Microsoft; Aviv Zohar, huji. Global Optimality of h&m delivery code, Local Search for what is a market, Low Rank Matrix Recovery.

Srinadh Bhojanapalli*, TTI Chicago; Behnam Neyshabur, TTI-Chicago; Nathan Srebro, Phased LSTM: Accelerating Recurrent Network Training for h&m delivery code, Long or Event-based Sequences. Daniel Neil*, Institute of Cloning Essay, Neuroinformatics; Michael Pfeiffer, Institute of code, Neuroinformatics; Shih-Chii Liu, Improving PAC Exploration Using the Median of Means. Jason Pazis*, MIT; Ronald Parr, ; Jonathan How, MIT. Infinite Hidden Semi-Markov Modulated Interaction Point Process. Matt Zhang*, Nicta; Peng Lin, Data61; Ting Guo, Data61; Yang Wang, Data61, CSIRO; Fang Chen, Data61, CSIRO. Cooperative Inverse Reinforcement Learning. Dylan Hadfield-Menell*, UC Berkeley; Stuart Russell, UC Berkeley; Pieter Abbeel, ; Anca Dragan, Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments. Ransalu Senanayake*, The University of Sydney; Lionel Ott, The University of about Hitler and the of Versailles, Sydney; Simon O'Callaghan, NICTA; Fabio Ramos, The University of code, Sydney.

Select-and-Sample for Cloning Trials Essay, Spike-and-Slab Sparse Coding. Abdul-Saboor Sheikh, University of Oldenburg; Jorg Lucke*, Tractable Operations for code, Arithmetic Circuits of Probabilistic Models. Yujia Shen*, ; Arthur Choi, ; Adnan Darwiche, Greedy Feature Construction. Dino Oglic*, University of what is a, Bonn; Thomas Gaertner, The University of h&m delivery, Nottingham. Mistake Bounds for Binary Matrix Completion.

Mark Herbster, ; Stephen Pasteris, UCL; Massimiliano Pontil*, Data driven estimation of Laplace-Beltrami operator. Frederic Chazal, INRIA; Ilaria Giulini, ; Bertrand Michel*, Tracking the Best Expert in Essay of Versailles Non-stationary Stochastic Environments. Chen-Yu Wei*, Academia Sinica; Yi-Te Hong, Academia Sinica; Chi-Jen Lu, Academia Sinica. Learning to learn by gradient descent by code gradient descent. Marcin Andrychowicz*, Google Deepmind; Misha Denil, ; Sergio Gomez, Google DeepMind; Matthew Hoffman, Google DeepMind; David Pfau, Google DeepMind; Tom Schaul, ; Nando Freitas, Google.

Kernel Observers: Systems-Theoretic Modeling and Inference of islam rituals, Spatiotemporally Evolving Processes. Hassan Kingravi, Pindrop Security, Harshal Maske, UIUC, Girish Chowdhary*, UIUC. Quantum Perceptron Models. Ashish Kapoor*, ; Nathan Wiebe, Microsoft Research; Krysta M. H&m Delivery Code? Svore, Guided Policy Search as Approximate Mirror Descent. William Montgomery*, University of tns earthing, Washington; Sergey Levine, University of h&m delivery code, Washington. The Power of Cloning Trials Essay, Optimization from h&m delivery code, Samples. Eric Balkanski*, Harvard University; Aviad Rubinstein, UC Berkeley; Yaron Singer, Deep Exploration via Bootstrapped DQN.

Ian Osband*, DeepMind; Charles Blundell, DeepMind; Alexander Pritzel, ; Benjamin Van Roy, A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization. Jingwei Liang*, GREYC, ENSICAEN; Jalal Fadili, ; Gabriel Peyre, Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages. Yin Cheng Ng*, University College London; Pawel Chilinski, University College London; Ricardo Silva, University College London. Convolutional Neural Fabrics. Shreyas Saxena*, INRIA; Jakob Verbeek, Navdeep Jaitly*, ; Quoc Le, ; Oriol Vinyals, ; Ilya Sutskever, ; David Sussillo, Google; Samy Bengio,

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. Aryan Mokhtari*, University of market system, Pennsylvania; Hadi Daneshmand, ETH Zurich; Aurelien Lucchi, ; Thomas Hofmann, ; Alejandro Ribeiro, University of Pennsylvania. A Sparse Interactive Model for h&m delivery code, Inductive Matrix Completion. Jin Lu, University of Connecticut; Guannan Liang, University of Connecticut; jiangwen Sun, University of what, Connecticut; Jinbo Bi*, University of Connecticut. Coresets for code, Scalable Bayesian Logistic Regression. Jonathan Huggins*, MIT; Trevor Campbell, MIT; Tamara Broderick, MIT. Agnostic Estimation for Misspecified Phase Retrieval Models. Matey Neykov*, Princeton University; Zhaoran Wang, Princeton University; Han Liu, Linear Relaxations for Finding Diverse Elements in Metric Spaces. Aditya Bhaskara*, University of Utah; Mehrdad Ghadiri, Sharif University of Technolog; Vahab Mirrokni, Google; Ola Svensson, EPFL.

Binarized Neural Networks. Itay Hubara*, Technion; Matthieu Courbariaux, Universite de Montreal; Daniel Soudry, Columbia University; Ran El-Yaniv, Technion; Yoshua Bengio, Universite de Montreal. On Local Maxima in the Population Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences. Chi Jin*, UC Berkeley; Yuchen Zhang, ; Sivaraman Balakrishnan, CMU; Martin Wainwright, UC Berkeley; Michael Jordan, Memory-Efficient Backpropagation Through Time. Audrunas Gruslys*, Google DeepMind; Remi Munos, Google DeepMind; Ivo Danihelka, ; Marc Lanctot, Google DeepMind; Alex Graves, Bayesian Optimization with Robust Bayesian Neural Networks. Jost Tobias Springenberg*, University of Hitler Effects of Treaty of Versailles, Freiburg; Aaron Klein, University of code, Freiburg; Stefan Falkner, University of Essay Hitler and the, Freiburg; Frank Hutter, University of h&m delivery, Freiburg. Learnable Visual Markers.

Oleg Grinchuk, Skolkovo Institute of Science and tns earthing, Technology; Vadim Lebedev, Skolkovo Institute of code, Science and Technology; Victor Lempitsky*, Fast Algorithms for Robust PCA via Gradient Descent. Xinyang Yi*, UT Austin; Dohyung Park, University of Punishment Is Barbaric, Texas at code Austin; Yudong Chen, ; Constantine Caramanis, One-vs-Each Approximation to Softmax for Scalable Estimation of islam rituals, Probabilities. Learning Deep Embeddings with Histogram Loss. Evgeniya Ustinova, Skoltech; Victor Lempitsky*, Spectral Learning of Dynamic Systems from Nonequilibrium Data. Hao Wu*, Free University of h&m delivery code, Berlin; Frank Noe,

Markov Chain Sampling in Discrete Probabilistic Models with Constraints. Chengtao Li*, MIT; Suvrit Sra, MIT; Stefanie Jegelka, MIT. Mapping Estimation for Cloning Trials Essay, Discrete Optimal Transport. Michael Perrot*, University of Saint-Etienne, laboratoire Hubert Curien; Nicolas Courty, ; Remi Flamary, ; Amaury Habrard, University of Saint-Etienne, Laboratoire Hubert Curien. BBO-DPPs: Batched Bayesian Optimization via Determinantal Point Processes.

Tarun Kathuria*, Microsoft Research; Amit Deshpande, ; Pushmeet Kohli, Protein contact prediction from h&m delivery code, amino acid co-evolution using convolutional networks for graph-valued images. Vladimir Golkov*, Technical University of Munich; Marcin Skwark, Vanderbilt University; Antonij Golkov, University of Augsburg; Alexey Dosovitskiy, ; Thomas Brox, University of Human Cloning Essay, Freiburg; Jens Meiler, Vanderbilt University; Daniel Cremers, Technical University of h&m delivery, Munich. Linear Feature Encoding for system, Reinforcement Learning. Zhao Song*, Duke University; Ronald Parr, ; Xuejun Liao, Duke University; Lawrence Carin, A Minimax Approach to h&m delivery, Supervised Learning. Farzan Farnia*, Stanford University; David Tse, Stanford University. Edge-Exchangeable Graphs and term for a student, Sparsity.

Diana Cai*, University of Chicago; Trevor Campbell, MIT; Tamara Broderick, MIT. A Locally Adaptive Normal Distribution. Georgios Arvanitidis*, DTU; Lars Kai Hansen, ; Soren Hauberg, Completely random measures for h&m delivery, modelling block-structured sparse networks. Tue Herlau*, ; Mikkel Schmidt, DTU; Morten Morup, Technical University of islam rituals, Denmark. Sparse Support Recovery with Non-smooth Loss Functions. Kevin Degraux*, Universite catholique de Louva; Gabriel Peyre, ; Jalal Fadili, ; Laurent Jacques, Universite catholique de Louvain. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Travis Monk*, University of h&m delivery code, Oldenburg; Cristina Savin, IST Austria; Jorg Lucke, Learning values across many orders of magnitude.

Hado Van Hasselt*, ; Arthur Guez, ; Matteo Hessel, Google DeepMind; Volodymyr Mnih, ; David Silver, Adaptive Smoothed Online Multi-Task Learning. Keerthiram Murugesan*, Carnegie Mellon University; Hanxiao Liu, Carnegie Mellon University; Jaime Carbonell, CMU; Yiming Yang, CMU. Safe Exploration in list term for a Finite Markov Decision Processes with Gaussian Processes. Matteo Turchetta, ETH Zurich; Felix Berkenkamp*, ETH Zurich; Andreas Krause, Probabilistic Linear Multistep Methods. Onur Teymur*, Imperial College London; Kostas Zygalakis, ; Ben Calderhead, Stochastic Three-Composite Convex Minimization. Alp Yurtsever*, EPFL; Bang Vu, ; Volkan Cevher, Using Fast Weights to Attend to the Recent Past. Jimmy Ba*, University of h&m delivery code, Toronto; Geoffrey Hinton, Google; Volodymyr Mnih, ; Joel Leibo, Google DeepMind; Catalin Ionescu, Google.

Maximal Sparsity with Deep Networks? Bo Xin*, Peking University; Yizhou Wang, Peking University; Wen Gao, peking university; David Wipf, Quantifying and tns earthing, Reducing Stereotypes in code Word Embeddings. Tolga Bolukbasi*, Boston University; Kai-Wei Chang, ; James Zou, ; Venkatesh Saligrama, ; Adam Kalai, Microsoft Research. beta-risk: a New Surrogate Risk for about Hitler and the of Treaty of Versailles, Learning from Weakly Labeled Data. Valentina Zantedeschi*, UJM Saint-Etienne, France; Remi Emonet, ; Marc Sebban,

Learning Additive Exponential Family Graphical Models via $ell_ $-norm Regularized M-Estimation. Xiaotong Yuan*, Nanjing University of Informat; Ping Li, ; Tong Zhang, ; Qingshan Liu, ; Guangcan Liu, NUIST. Backprop KF: Learning Discriminative Deterministic State Estimators. Tuomas Haarnoja*, UC Berkeley; Anurag Ajay, UC Berkeley; Sergey Levine, University of Washington; Pieter Abbeel, 2-Component Recurrent Neural Networks. Xiang Li*, NJUST; Tao Qin, Microsoft; Jian Yang, ; Xiaolin Hu, ; Tie-Yan Liu, Microsoft Research. Fast recovery from a union of code, subspaces. Chinmay Hegde, ; Piotr Indyk, MIT; Ludwig Schmidt*, MIT. Incremental Learning for Variational Sparse Gaussian Process Regression. Ching-An Cheng*, Georgia Institute of Technolog; Byron Boots, A Consistent Regularization Approach for Structured Prediction.

Carlo Ciliberto*, MIT; Lorenzo Rosasco, ; Alessandro Rudi, Clustering Signed Networks with the market, Geometric Mean of Laplacians. Pedro Eduardo Mercado Lopez*, Saarland University; Francesco Tudisco, Saarland University; Matthias Hein, Saarland University. An urn model for code, majority voting in classification ensembles. Victor Soto, Columbia University; Alberto Suarez, ; Gonzalo Martinez-Munoz*,

Avoiding Imposters and Cloning Trials, Delinquents: Adversarial Crowdsourcing and Peer Prediction. Jacob Steinhardt*, Stanford University; Gregory Valiant, ; Moses Charikar, Stanford University. Fast and code, accurate spike sorting of high-channel count probes with KiloSort. Marius Pachitariu*, ; Nick Steinmetz, UCL; Shabnam Kadir, ; Matteo Carandini, UCL; Kenneth Harris, UCL. Combining Adversarial Guarantees and Essay Hitler and the Effects of Treaty of Versailles, Stochastic Fast Rates in Online Learning. Wouter M. Koolen*, ; Peter Grunwald, CWI; Tim Van Erven, Ancestral Causal Inference.

Sara Magliacane*, VU University Amsterdam; Tom Claassen, ; Joris Mooij, Radboud University Nijmegen. More Supervision, Less Computation: Statistical-Computational Tradeoffs in h&m delivery code Weakly Supervised Learning. Xinyang Yi, UT Austin; Zhaoran Wang, Princeton University; Zhuoran Yang , Princeton University; Constantine Caramanis, ; Han Liu*, Tagger: Deep Unsupervised Perceptual Grouping. Klaus Greff*, IDSIA; Antti Rasmus, The Curious AI Company; Mathias Berglund, The Curious AI Company; Tele Hao, The Curious AI Company; Harri Valpola, The Curious AI Company. Efficient Algorithm for Trials, Streaming Submodular Cover. Ashkan Norouzi-Fard*, EPFL; Abbas Bazzi, EPFL; Ilija Bogunovic, EPFL Lausanne; Marwa El Halabi, l; Ya-Ping Hsieh, ; Volkan Cevher, Interaction Networks for Learning about code, Objects, Relations and what is a market, Physics. Peter Battaglia*, Google DeepMind; Razvan Pascanu, ; Matthew Lai, Google DeepMind; Danilo Jimenez Rezende, ; Koray Kavukcuoglu, Google DeepMind. Efficient state-space modularization for planning: theory, behavioral and code, neural signatures. Daniel McNamee*, University of Capital Punishment, Cambridge; Daniel Wolpert, University of Cambridge; Mate Lengyel, University of code, Cambridge.

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. Chi Jin*, UC Berkeley; Sham Kakade, ; Praneeth Netrapalli, Microsoft Research. Online Bayesian Moment Matching for Punishment Is Barbaric Essay, Topic Modeling with Unknown Number of code, Topics. Wei-Shou Hsu*, University of Waterloo; Pascal Poupart, Computing and maximizing influence in linear threshold and triggering models.

Justin Khim*, University of Cloning Trials, Pennsylvania; Varun Jog, ; Po-Ling Loh, Berkeley. Coevolutionary Latent Feature Processes for code, Continuous-Time User-Item Interactions. Yichen Wang*, Georgia Tech; Nan Du, ; Rakshit Trivedi, Georgia Institute of what market, Technolo; Le Song, Learning Deep Parsimonious Representations. Renjie Liao*, UofT; Alexander Schwing, ; Rich Zemel, ; Raquel Urtasun, Optimal Learning for Multi-pass Stochastic Gradient Methods.

Junhong Lin*, Istituto Italiano di Tecnologia; Lorenzo Rosasco, Generative Adversarial Imitation Learning. Jonathan Ho*, Stanford; Stefano Ermon, An End-to-End Approach for h&m delivery, Natural Language to IFTTT Program Translation. Chang Liu*, University of tns earthing, Maryland; Xinyun Chen, Shanghai Jiaotong University; Richard Shin, ; Mingcheng Chen, University of code, Illinois, Urbana-Champaign; Dawn Song, UC Berkeley. Dual Space Gradient Descent for Online Learning. Trung Le*, University of what system, Pedagogy Ho Chi Minh city; Tu Nguyen, Deakin University; Vu Nguyen, Deakin University; Dinh Phung, Deakin University. Fast stochastic optimization on Riemannian manifolds. Hongyi Zhang*, MIT; Sashank Jakkam Reddi, Carnegie Mellon University; Suvrit Sra, MIT.

Professor Forcing: A New Algorithm for h&m delivery code, Training Recurrent Networks. Alex Lamb, Montreal; Anirudh Goyal*, University of Montreal; ying Zhang, University of list term for a, Montreal; Saizheng Zhang, University of Montreal; Aaron Courville, University of Montreal; Yoshua Bengio, U. H&m Delivery? Montreal. Learning brain regions via large-scale online structured sparse dictionary learning. Elvis DOHMATOB*, Inria; Arthur Mensch, inria; Gael Varoquaux, ; Bertrand Thirion, Efficient Neural Codes under Metabolic Constraints. Zhuo Wang*, University of islam rituals, Pennsylvania; Xue-Xin Wei, University of code, Pennsylvania; Alan Stocker, ; Dan Lee , University of Pennsylvania. Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods. Andrej Risteski*, Princeton University; Yuanzhi Li, Princeton University. Efficient High-Order Interaction-Aware Feature Selection Based on islam rituals, Conditional Mutual Information. Alexander Shishkin, Yandex; Anastasia Bezzubtseva, Yandex; Alexey Drutsa*, Yandex; Ilia Shishkov, Yandex; Ekaterina Gladkikh, Yandex; Gleb Gusev, Yandex LLC; Pavel Serdyukov, Yandex.

Bayesian Intermittent Demand Forecasting for code, Large Inventories. Matthias Seeger*, Amazon; David Salinas, Amazon; Valentin Flunkert, Amazon. Visual Question Answering with Question Representation Update. RUIYU LI*, CUHK; Jiaya Jia, CUHK. Learning Parametric Sparse Models for Essay about and the Effects of Treaty of Versailles, Image Super-Resolution. Yongbo Li, Xidian University; Weisheng Dong*, Xidian University; GUANGMING Shi, Xidian University; Xuemei Xie, Xidian University; Xin Li, WVU. Blazing the h&m delivery, trails before beating the path: Sample-efficient Monte-Carlo planning.

Jean-Bastien Grill, Inria Lille - Nord Europe; Michal Valko*, Inria Lille - Nord Europe; Remi Munos, Google DeepMind. Asynchronous Parallel Greedy Coordinate Descent. Yang You, UC Berkeley; Xiangru Lian, University of Essay about Effects of Treaty of Versailles, Rochester; Cho-Jui Hsieh*, ; Ji Liu, ; Hsiang-Fu Yu, University of Texas at code Austin; Inderjit Dhillon, ; James Demmel, UC Berkeley. Iterative Refinement of the Essay, Approximate Posterior for Directed Belief Networks. Rex Devon Hjelm*, University of code, New Mexico; Ruslan Salakhutdinov, University of Toronto; Kyunghyun Cho, University of Hitler Effects, Montreal; Nebojsa Jojic, Microsoft Research; Vince Calhoun, Mind Research Network; Junyoung Chung, University of Montreal. Assortment Optimization Under the Mallows model. Antoine Desir*, Columbia University; Vineet Goyal, ; Srikanth Jagabathula, ; Danny Segev, Disease Trajectory Maps.

Peter Schulam*, Johns Hopkins University; Raman Arora, Multistage Campaigning in Social Networks. Mehrdad Farajtabar*, Georgia Tech; Xiaojing Ye, Georgia State University; Sahar Harati, Emory University; Le Song, ; Hongyuan Zha, Georgia Institute of Technology. Learning in Games: Robustness of h&m delivery code, Fast Convergence. Dylan Foster, Cornell University; Zhiyuan Li, Tsinghua University; Thodoris Lykouris*, Cornell University; Karthik Sridharan, Cornell University; Eva Tardos, Cornell University. Improving Variational Autoencoders with Inverse Autoregressive Flow.

Diederik Kingma*, ; Tim Salimans, Algorithms and matching lower bounds for tns earthing, approximately-convex optimization. Andrej Risteski*, Princeton University; Yuanzhi Li, Princeton University. Unified Methods for Exploiting Piecewise Structure in Convex Optimization. Tyler Johnson*, University of h&m delivery code, Washington; Carlos Guestrin,

Kernel Bayesian Inference with Posterior Regularization. Yang Song*, Stanford University; Jun Zhu, ; Yong Ren, Tsinghua University. Neural universal discrete denoiser. Taesup Moon*, DGIST; Seonwoo Min, Seoul National University; Byunghan Lee, Seoul National University ; Sungroh Yoon, Seoul National University Optimal Architectures in tns earthing a Solvable Model of Deep Networks. Jonathan Kadmon*, Hebrew University; Haim Sompolinsky , Conditional Image Generation with Pixel CNN Decoders. Aaron Van den Oord*, Google Deepmind; Nal Kalchbrenner, ; Lasse Espeholt, ; Koray Kavukcuoglu, Google DeepMind; Oriol Vinyals, ; Alex Graves, Supervised Learning with Tensor Networks. Edwin Stoudenmire*, Univ of code, California Irvine; David Schwab, Northwestern University.

Multi-step learning and underlying structure in statistical models. Maia Fraser*, University of list of short term, Ottawa. Blind Optimal Recovery of h&m delivery code, Signals. Dmitry Ostrovsky*, Univ. List For A? Grenoble Alpes; Zaid Harchaoui, NYU, Courant Institute; Anatoli Juditsky, ; Arkadi Nemirovski, Gerogia Institute of code, Technology. An Architecture for term student, Deep, Hierarchical Generative Models. Feature selection for h&m delivery code, classification of functional data using recursive maxima hunting. Jose Torrecilla*, Universidad Autonoma de Madrid; Alberto Suarez,

Achieving budget-optimality with adaptive schemes in crowdsourcing. Ashish Khetan, University of list goals for a, Illinois Urbana-; Sewoong Oh*, Near-Optimal Smoothing of Structured Conditional Probability Matrices. Moein Falahatgar, UCSD; Mesrob I. H&m Delivery? Ohannessian*, ; Alon Orlitsky, Supervised Word Mover's Distance. Gao Huang, ; Chuan Guo*, Cornell University; Matt Kusner, ; Yu Sun, ; Fei Sha, University of Human Trials Essay, Southern California; Kilian Weinberger, Exploiting Tradeoffs for Exact Recovery in h&m delivery code Heterogeneous Stochastic Block Models. Amin Jalali*, University of is a market, Washington; Qiyang Han, University of code, Washington; Ioana Dumitriu, University of Washington; Maryam Fazel, University of Washington. Full-Capacity Unitary Recurrent Neural Networks. Scott Wisdom*, University of list term for a student, Washington; Thomas Powers, ; John Hershey, ; Jonathan LeRoux, ; Les Atlas,

Threshold Bandits, With and Without Censored Feedback. Jacob Abernethy, ; Kareem Amin, ; Ruihao Zhu*, Massachusetts Institute of code, Technology. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. Wenjie Luo*, University of Toronto; Yujia Li, University of tns earthing, Toronto; Raquel Urtasun, ; Rich Zemel, Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods. Lev Bogolubsky, ; Pavel Dvurechensky*, Weierstrass Institute for h&m delivery code, Appl; Alexander Gasnikov, ; Gleb Gusev, Yandex LLC; Yurii Nesterov, ; Andrey Raigorodskii, ; Aleksey Tikhonov, ; Maksim Zhukovskii, k^*-Nearest Neighbors: From Global to Local. Oren Anava, Technion; Kfir Levy*, Technion.

Normalized Spectral Map Synchronization. Yanyao Shen*, UT Austin; Qixing Huang, Toyota Technological Institute at Chicago; Nathan Srebro, ; Sujay Sanghavi, Beyond Exchangeability: The Chinese Voting Process. Moontae Lee*, Cornell University; Seok Hyun Jin, Cornell University; David Mimno, Cornell University. A posteriori error bounds for term student, joint matrix decomposition problems. Nicolo Colombo, Univ of code, Luxembourg; Nikos Vlassis*, Adobe Research. A Bayesian method for tns earthing, reducing bias in h&m delivery neural representational similarity analysis. Ming Bo Cai*, Princeton University; Nicolas Schuck, Princeton Neuroscience Institute, Princeton University; Jonathan Pillow, ; Yael Niv, Online ICA: Understanding Global Dynamics of Essay, Nonconvex Optimization via Diffusion Processes.

Chris Junchi Li, Princeton University; Zhaoran Wang*, Princeton University; Han Liu, Following the h&m delivery, Leader and Capital Is Barbaric Essay, Fast Rates in code Linear Prediction: Curved Constraint Sets and Capital Is Barbaric Essay, Other Regularities. Ruitong Huang*, University of code, Alberta; Tor Lattimore, ; Andras Gyorgy, ; Csaba Szepesvari, U. Is A System? Alberta. SDP Relaxation with Randomized Rounding for Energy Disaggregation. Kiarash Shaloudegi, ; Andras Gyorgy*, ; Csaba Szepesvari, U. H&m Delivery? Alberta; Wilsun Xu, University of Alberta. Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates. Yuanzhi Li, Princeton University; Yingyu Liang*, ; Andrej Risteski, Princeton University.

Unsupervised Learning of Human Essay, 3D Structure from Images. Danilo Jimenez Rezende*, ; S. M. H&m Delivery? Ali Eslami, Google DeepMind; Shakir Mohamed, Google DeepMind; Peter Battaglia, Google DeepMind; Max Jaderberg, ; Nicolas Heess, Poisson-Gamma dynamical systems. Aaron Schein*, UMass Amherst; Hanna Wallach, Microsoft Research; Mingyuan Zhou, Gaussian Processes for Survival Analysis. Tamara Fernandez, Oxford; Nicolas Rivera*, King's College London; Yee-Whye Teh,

Dual Decomposed Learning with Factorwise Oracle for Essay about and the of Versailles, Structural SVM of Large Output Domain. Ian En-Hsu Yen*, University of h&m delivery, Texas at islam rituals Austin; huang Xiangru, University of h&m delivery, Texas at Austin; Kai Zhong, University of Essay and the, Texas at Austin; Zhang Ruohan, University of Texas at Austin; Pradeep Ravikumar, ; Inderjit Dhillon, Optimal Binary Classifier Aggregation for General Losses. Akshay Balsubramani*, UC San Diego; Yoav Freund, Disentangling factors of h&m delivery code, variation in Human Cloning Trials deep representation using adversarial training. Michael Mathieu, NYU; Junbo Zhao, NYU; Aditya Ramesh, NYU; Pablo Sprechmann*, ; Yann LeCun, NYU. A primal-dual method for h&m delivery, constrained consensus optimization. Necdet Aybat*, Penn State University; Erfan Yazdandoost Hamedani, Penn State University. Fundamental Limits of islam rituals, Budget-Fidelity Trade-off in h&m delivery code Label Crowdsourcing.

Farshad Lahouti *, Caltech ; Babak Hassibi, Caltech.