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MMLS 2017 Booklet
MMLS 2017 Booklet Monday, June 19 9:30 - 11:15 TTIC Continental breakfast. (TTIC Colloquium: 10:00-11:00.) 11:15 - 11:30 GPAH Opening remarks: Po-Ling Loh. 11:30 - 12:20 GPAH Plenary speaker: Devavrat Shah . (Chair: Po-Ling Loh .) Latent Variable Model Estimation via Collaborative Filtering. 12:20 - 2:50 GPAH Lunch, posters. 2:50 - 3:30 GPAH Invited talks (chair: Mesrob Ohannessian ). 2:50: Rina Foygel Projected Gradient Descent with Nonconvex Constraints. 3:10: Maxim Raginsky Non-Convex Learning via Stochastic Gradient Langevin Dynamics. 3:30 - 4:00 GPAH Coffee Break. 4:00 - 4:50 GPAH Plenary speaker: Rayid Ghani . (Chair: Nati Srebro .) Machine Learning for Public Policy: Opportunities and Challenges 4:50 - 5:30 GPAH Invited talks (chair: Laura Balzano ). 4:50: Dimitris Papailiopoulos Gradient Diversity Empowers Distributed Learning. 5:10: Alan Ritter Large-Scale Learning for Information Extraction. 5:45 - 7:00 TTIC Reception, with remarks by Sadaoki Furui (TTIC President). Tuesday, June 20 8:30 - 9:50 GPAH Continental breakfast. 9:00 - 9:50 GPAH Bonus speaker: Larry Wasserman . (Chair: Mladen Kolar .) Locally Optimal Testing. [ Cancelled. ] 9:50 - 10:50 GPAH Invited talks (chair: Misha Belkin ). 9:50: Srinadh Bhojanapalli Effectiveness of Local Search for Low Rank Recovery 10:10: Niao He Learning From Conditional Distributions. 10:30: Clayton Scott Nonparametric Preference Completion. 10:50 - 11:20 GPAH Coffee break. 11:20 - 12:20 GPAH Invited talks (chair: Jason Lee ). 11:20: Lev Reyzin On the Complexity of Learning from Label Proportions. 11:40: Ambuj Tewari Random Perturbations in Online Learning. 12:00: Risi Kondor Multiresolution Matrix Factorization. -
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Theory and Algorithms for Modern Problems in Machine Learning and an Analysis of Markets
Theory and Algorithms for Modern Problems in Machine Learning and an Analysis of Markets by Ashish Rastogi A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science Courant Institute of Mathematical Sciences New York University May 2008 Richard Cole—Advisor Mehryar Mohri—Advisor °c Ashish Rastogi All Rights Reserved, 2008 To the most wonderful parents in the whole world, Mrs. Asha Rastogi and Mr. Shyam Lal Rastogi iv Acknowledgements First and foremost, I would like to thank my advisors, Professor Richard Cole and Professor Mehryar Mohri, for their unwavering support, guidance and constant encouragement. They have been inspiring mentors and much of what lies in the following pages can be credited to them. Working under their supervision has been one of the most enriching experiences of my life. I would also like to thank Professor Joel Spencer, Professor Arun Sun- dararajan, Professor Subhash Khot and Dr. Corinna Cortes for agreeing to serve as members on my thesis committee. Professor Spencer’s class on Random Graphs remains one of the most stim- ulating courses I undertook as a graduate student. Internships at Google through the summers of 2005, 2006 and 2007 were some of the most enjoy- able periods of my graduate school life. Many thanks are due to Dr. Corinna Cortes for providing me with the opportunity to work on several challenging problems at Google. Research initiated during these internships culminated in the development of ideas that form the bulk of this thesis. I would also like to thank my peers from the graduate school. -
Session II – Unit I – Fundamentals
Tutor:- Prof. Avadhoot S. Joshi Subject:- Design & Analysis of Algorithms Session II Unit I - Fundamentals • Algorithm Design – II • Algorithm as a Technology • What kinds of problems are solved by algorithms? • Evolution of Algorithms • Timeline of Algorithms Algorithm Design – II Algorithm Design (Cntd.) Figure briefly shows a sequence of steps one typically goes through in designing and analyzing an algorithm. Figure: Algorithm design and analysis process. Algorithm Design (Cntd.) Understand the problem: Before designing an algorithm is to understand completely the problem given. Read the problem’s description carefully and ask questions if you have any doubts about the problem, do a few small examples by hand, think about special cases, and ask questions again if needed. There are a few types of problems that arise in computing applications quite often. If the problem in question is one of them, you might be able to use a known algorithm for solving it. If you have to choose among several available algorithms, it helps to understand how such an algorithm works and to know its strengths and weaknesses. But often you will not find a readily available algorithm and will have to design your own. An input to an algorithm specifies an instance of the problem the algorithm solves. It is very important to specify exactly the set of instances the algorithm needs to handle. Algorithm Design (Cntd.) Ascertaining the Capabilities of the Computational Device: After understanding the problem you need to ascertain the capabilities of the computational device the algorithm is intended for. The vast majority of algorithms in use today are still destined to be programmed for a computer closely resembling the von Neumann machine—a computer architecture outlined by the prominent Hungarian-American mathematician John von Neumann (1903–1957), in collaboration with A. -
The Boosting Apporach to Machine Learning
Nonlinear Estimation and Classification, Springer, 2003. The Boosting Approach to Machine Learning An Overview Robert E. Schapire AT&T Labs Research Shannon Laboratory 180 Park Avenue, Room A203 Florham Park, NJ 07932 USA www.research.att.com/ schapire December 19, 2001 Abstract Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting. 1 Introduction Machine learning studies automatic techniques for learning to make accurate pre- dictions based on past observations. For example, suppose that we would like to build an email filter that can distinguish spam (junk) email from non-spam. The machine-learning approach to this problem would be the following: Start by gath- ering as many examples as posible of both spam and non-spam emails. Next, feed these examples, together with labels indicating if they are spam or not, to your favorite machine-learning algorithm which will automatically produce a classifi- cation or prediction rule. Given a new, unlabeled email, such a rule attempts to predict if it is spam or not. The goal, of course, is to generate a rule that makes the most accurate predictions possible on new test examples. 1 Building a highly accurate prediction rule is certainly a difficult task. -
Doctor of Philosophy
RICE UNIVERSITY By Randall Balestriero A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE Doctor of Philosophy APPROVED, THESIS COMMITTEE Richard Baraniuk (Apr 28, 2021 06:57 ADT) Ankit B Patel (Apr 28, 2021 02:34 CDT) Ankit Patel Richard Baraniuk Behnam Azhang Behnam Azhang (Apr 26, 2021 18:34 CDT) Behnaam Aazhang Stephane Mallat Moshe Vardi Moshe Vardi (Apr 26, 2021 20:04 CDT) Albert Cohen (Apr 27, 2021 16:36 GMT+2) Moshe Vardi Albert Cohen HOUSTON, TEXAS April 2021 ABSTRACT Max-Affine Splines Insights Into Deep Learning by Randall Balestriero We build a rigorous bridge between deep networks (DNs) and approximation the- ory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition of max-affine spline operators (MASOs), which provide a powerful portal through which to view and analyze their inner workings. For instance, conditioned on the spline partition region containing the input signal, the output of a MASO DN can be written as a simple affine transformation of the input. Studying the geometry of those regions allows to obtain novel insights into different regular- ization techniques, different layer configurations or different initialization schemes. Going further, this spline viewpoint allows to obtain precise geometric insights in various domains such as the characterization of the Deep Generative Networks's gen- erated manifold, the understanding of Deep Network pruning as a mean to simplify the DN input space partition or the relationship between different nonlinearities e.g. ReLU-Sigmoid Gated Linear Unit as simply corresponding to different MASO region membership inference algorithms. -
Algorithms and Hardness for Linear Algebra on Geometric Graphs
Algorithms and Hardness for Linear Algebra on Geometric Graphs∗ Josh Alman† Timothy Chu‡ Aaron Schild§ Zhao Song¶ Abstract d d d For a function K : R R R≥ , and a set P = x ,...,xn R of n points, the K × → 0 { 1 } ⊂ graph GP of P is the complete graph on n nodes where the weight between nodes i and j is given by K(xi, xj ). In this paper, we initiate the study of when efficient spectral graph theory is possible on these graphs. We investigate whether or not it is possible to solve the following 1+o(1) o(1) problems in n time for a K-graph GP when d<n : • Multiply a given vector by the adjacency matrix or Laplacian matrix of GP • Find a spectral sparsifier of GP • Solve a Laplacian system in GP ’s Laplacian matrix 2 For each of these problems, we consider all functions of the form K(u, v) = f( u v 2) for a function f : R R. We provide algorithms and comparable hardness resultsk for−manyk such K, including the→ Gaussian kernel, Neural tangent kernels, and more. For example, in dimension d = Ω(log n), we show that there is a parameter associated with the function f for which low parameter values imply n1+o(1) time algorithms for all three of these problems and high parameter values imply the nonexistence of subquadratic time algorithms assuming Strong Exponential Time Hypothesis (SETH), given natural assumptions on f. As part of our results, we also show that the exponential dependence on the dimension d in the celebrated fast multipole method of Greengard and Rokhlin cannot be improved, assuming SETH, for a broad class of functions f. -
Algorithms and Hardness for Linear Algebra on Geometric Graphs
2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS) Algorithms and Hardness for Linear Algebra on Geometric Graphs Josh Alman Timothy Chu Aaron Schild Zhao Song [email protected] [email protected] [email protected] [email protected] Harvard U. Carnegie Mellon U. U. of Washington Columbia, Princeton, IAS d d Abstract—For a function K : R × R → R≥0, and a set learning. The first task is a celebrated application of the P {x ,...,x }⊂Rd n K G P = 1 n of points, the graph P of fast multipole method of Greengard and Rokhlin [GR87], n is the complete graph on nodes where the weight between [GR88], [GR89], voted one of the top ten algorithms of the nodes i and j is given by K(xi,xj ). In this paper, we initiate the study of when efficient spectral graph theory is possible twentieth century by the editors of Computing in Science and on these graphs. We investigate whether or not it is possible Engineering [DS00]. The second task is spectral clustering to solve the following problems in n1+o(1) time for a K-graph [NJW02], [LWDH13], a popular algorithm for clustering o(1) GP when d<n : data. The third task is to label a full set of data given only • Multiply a given vector by the adjacency matrix or a small set of partial labels [Zhu05b], [CSZ09], [ZL05], Laplacian matrix of GP which has seen increasing use in machine learning. One • G Find a spectral sparsifier of P notable method for performing semi-supervised learning is • Solve a Laplacian system in GP ’s Laplacian matrix the graph-based Laplacian regularizer method [LSZ+19b], For each of these problems, we consider all functions of the K u, v f u − v2 f R → R [ZL05], [BNS06], [Zhu05a]. -
Contents U U U
Contents u u u ACM Awards Reception and Banquet, June 2018 .................................................. 2 Introduction ......................................................................................................................... 3 A.M. Turing Award .............................................................................................................. 4 ACM Prize in Computing ................................................................................................. 5 ACM Charles P. “Chuck” Thacker Breakthrough in Computing Award ............. 6 ACM – AAAI Allen Newell Award .................................................................................. 7 Software System Award ................................................................................................... 8 Grace Murray Hopper Award ......................................................................................... 9 Paris Kanellakis Theory and Practice Award ...........................................................10 Karl V. Karlstrom Outstanding Educator Award .....................................................11 Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics ..........................................................12 Distinguished Service Award .......................................................................................13 ACM Athena Lecturer Award ........................................................................................14 Outstanding Contribution -
12 | Learning Theory
12 | LEARNING THEORY The Universe is under no obligation to make sense to you. – Learning Objectives: Neil deGrasse Tyson • Explain why inductive bias is necessary. • Define the PAC model and explain why both the “P” and “A” are necessary. • Explain the relationship between complexity measures and regulariz- ers. By now, you are an expert at building learning algorithms. You • Identify the role of complexity in probably understand how they work, intuitively. And you under- generalization. stand why they should generalize. However, there are several basic • Formalize the relationship between margins and complexity. questions you might want to know the answer to. Is learning always possible? How many training examples will I need to do a good job learning? Is my test performance going to be much worse than my training performance? The key idea that underlies all these answer is that simple functions generalize well. The amazing thing is that you can actually prove strong results that address the above questions. In this chapter, you will learn some of the most important results in learning theory that attempt to answer these questions. The goal of this chapter is not theory for Dependencies: theory’s sake, but rather as a way to better understand why learning models work, and how to use this theory to build better algorithms. As a concrete example, we will see how 2-norm regularization prov- ably leads to better generalization performance, thus justifying our common practice! 12.1 The Role of Theory In contrast to the quote at the start of this chapter, a practitioner friend once said “I would happily give up a few percent perfor- mance for an algorithm that I can understand.” Both perspectives are completely valid, and are actually not contradictory. -
Association for Computing Machinery 2 Penn Plaza, Suite 701, New York
ACM A N W N A N R N D N S Association for Computing Machinery 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA +1-212-869-7440 www.acm.org Contents N N N ACM Awards Reception and Banquet, June 2017 . .2 Introduction . .3 A.M. Turing Award . .4 ACM Prize in Computing . .5 ACM – AAAI Allen Newell Award . .6 Software System Award . .7 Grace Murray Hopper Award . .8 Paris Kanellakis Theory and Practice Award . .9 Karl V. Karlstrom Outstanding Educator Award . .10 ACM Policy Award . .11 Distinguished Service Award . .12 ACM Athena Lecturer Award . .13 Outstanding Contribution to ACM Award . .14 ACM Presidential Award . .15-17 Doctoral Dissertation Award . .18 ACM Student Research Competition . .19 ACM Fellows . .20 Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics . .21 ACM Gordon Bell Prize . .21 ACM – IEEE CS Eckert-Mauchly Award . .22 ACM – IEEE CS Ken Kennedy Award . .22 ACM – IEEE CS George Michael HPC Memorial Fellowships . .23 SIAM – ACM Prize in Computational Science and Engineering . .23 ACM – CSTA Cutler-Bell Prize . .24 ACM India Doctoral Dissertation Award . .24 ACM China Doctoral Dissertation Award . .25 ACM China Rising Star Award . .25 IPSJ/ACM Award for Early Career Contributions to Global Research . .25 ACM Special Interest Group Awards . .26-27 2017 ACM Award Subcommittee Chairs . .28 ACM Award Nomination Submission Procedures . .29 2018 ACM Award Subcommittee Chairs and Members . .30-31 Past Recipients . .32-36 ACM Fellows . .37-43 In Memoriam, ACM Fellows . .44 1 ACM Awards Reception & Banquet ACM AWARDS N N N N N N The Westin St. -
A Short Introduction to Boosting
Journal of Japanese Society for Artificial Intelligence,14(5):771-780, September, 1999. (In Japanese, translation by Naoki Abe.) A Short Introduction to Boosting Yoav Freund Robert E. Schapire AT&T Labs Research Shannon Laboratory 180 Park Avenue Florham Park, NJ 07932 USA ¢ ¡ www.research.att.com/ yoav, schapire £ ¢ yoav, schapire £ @research.att.com Abstract Boosting is a general method for improving the accuracy of any given learning algorithm. This short overview paper introduces the boosting algorithm AdaBoost, and explains the un- derlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting's relationship to support-vector machines. Some examples of recent applications of boosting are also described. Introduction A horse-racing gambler, hoping to maximize his winnings, decides to create a computer program that will accurately predict the winner of a horse race based on the usual information (number of races recently won by each horse, betting odds for each horse, etc.). To create such a program, he asks a highly successful expert gambler to explain his betting strategy. Not surprisingly, the expert is unable to articulate a grand set of rules for selecting a horse. On the other hand, when presented with the data for a specific set of races, the expert has no trouble coming up with a “rule of thumb” for that set of races (such as, “Bet on the horse that has recently won the most races” or “Bet on the horse with the most favored odds”). Although such a rule of thumb, by itself, is obviously very rough and inaccurate, it is not unreasonable to expect it to provide predictions that are at least a little bit better than random guessing.