• Click on the titles of individual talks for abstract, slides and archived video.
  • The workshop is oversubscribed; only registered participants are permitted to attend.
  • We will be using an overflow room showing the live video feed in order to accommodate as many registered participants as possible. Seating in the auditorium is not guaranteed and will be on a first-come, first-served basis for registered participants only. We strongly suggest that participants arrive early for each talk to secure seating in the auditorium. Doors will open each day at 8:30 a.m.
  • Talks will also be streamed live on this page during the workshop. If you did not register, we hope that you will be able to either join us for the live webcast or enjoy the videos at a later time.

All events take place in the Calvin Lab Auditorium.

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Coffee and Check-In
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Opening Remarks
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Introductions
<b>Sparsity and Base Learning</b>
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Low-Rank Matrix Recovery Through Rank-One Projections
Tony Cai, University of Pennsylvania
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Sketching for M-Estimators: A Unified Approach to Robust Regression
David Woodruff (Carnegie Mellon University)
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Break
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Simple, Efficient and Neural Algorithms for Sparse Coding
Ankur Moitra, Massachusetts Institute of Technology
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Learning a Hidden Basis through Imperfect Measurements: Why and How
Misha Belkin (UCSD)
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Lunch
<b>Clustering and Community Organization</b>
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Constrained Rank Aggregation and Correlation Clustering
Olgica Milenkovic (University of Illinois at Urbana-Champaign)
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Testing Distribution Families
Jayadev Acharya, Massachusetts Institute of Technology
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Break
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Fundamental Limit of Community Detection in Stochastic Block Models
Emmanuel Abbe (Princeton University)
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How Large is the Norm of a Random Matrix?
Ramon van Handel, Princeton University
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Nearly Linear-Time Algorithms for Structured Sparsity
Piotr Indyk (Massachusetts Institute of Technology)
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Discussion, Open Problems and Lucky Talk Selection
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Reception
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Coffee and Check-In
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Lucky Talk: From Coding to Clustering
Emmanuel Abbe (Princeton University)
<b>Distribution Properties</b>
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Chebyshev Polynomials, Moment Matching and Optimal Estimation of the Unseen
Yihong Wu (Yale University)
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Information Measure Estimation and Applications: Boosting the Sample Size from n to n log n
Jiantao Jiao, Stanford University
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Break
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Sample Complexity of Estimating Entropy
Himanshu Tyagi, Indian Institute of Science
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Minimum Rényi Correlation Principle: From Marginals to Joint Distribution
Farzan Farnia, Stanford University
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Lunch
<b>Sequential Estimation and Compression</b>
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Adaptive Coding over Countable Alphabets
Stéphane Boucheron, Université Paris Diderot
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On Learning Distributions from their Samples
Sudeep Kamath, Princeton University
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Break
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Efficient Minimax Optimal Strategies for Universal Prediction
Peter Bartlett (Simons Institute, UC Berkeley)
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On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
Aurélien Garivier, University of Toulouse
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Discussion, Open Problems and Lucky Talk Selection
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Coffee and Check-In
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Lucky Talk: Chebyshev Polynomials, Moment Matching and Optimal Estimation of the Unseen
Yihong Wu (Yale University)
<b>Information Aggregation and Pattern Matching</b>
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Low Regret Recommendations and Item-Item Collaborative Filtering
Devavrat Shah, Massachusetts Institute of Technology
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Topic Modeling Approach for Rank Aggregation
Venkat Saligrama, Boston University
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Break
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Machine Learning from Human Comparative Judgments
Robert Nowak (University of Wisconsin-Madison)
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Analytic Pattern Matching: From DNA to Twitter
Wojciech Szpankowski, Purdue University
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Lunch
<b>Learning and Information Theory</b>
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Strong Data Processing Inequalities and Estimation with Constraints
John Duchi (Stanford University)
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Strong Data Processing Inequalities: Applications to MCMC and Graphical Models
Maxim Raginsky, University of Illinois, Urbana-Champaign
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Break
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Near-Optimal Hypothesis Testing via Convex Optimization
Arkadi Nemirovski, Georgia Institute of Technology
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Data Driven Convergence in Statistical Estimators
Narayana Santhanam (University of Hawaii)
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Discussion, Open Problems and Lucky Talk Selection
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Coffee and Check-In
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Lucky Talk: Low-regret Recommendations and Item-item Collaborative Filtering
Guy Bresler (Massachusetts Institute of Technology), Devavrat Shah, Massachusetts Institute of Technology
<b>Optimization and Learning Models</b>
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Confidence-Based Active Learning
Kamalika Chaudhuri (UC San Diego)
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Constructing Informative Features for Discriminative Learning
Animashree Anandkumar, UC Irvine
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Robust Inference and Local Algorithms
Yishay Mansour, Tel Aviv University
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Break
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Tradeoffs in Large Scale Learning: Statistical Accuracy vs. Numerical Precision
Sham Kakade, Microsoft Research New England
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Computation-Statistics Tradeoffs in Unsupervised Learning via Data Summarization
Mesrob Ohannessian (UC San Diego)
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Lunch
<b> Learning Structured Distributions</b>
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Statistical Property Testing and Estimation Beyond the i.i.d. Setting
Gregory Valiant (Stanford University)
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Nearest Neighbor Based Greedy Coordinate Descent
Pradeep Ravikumar, University of Texas, Austin
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Break
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Learning and Testing Structured Distributions
Ilias Diakonikolas (University of Wisconsin-Madison)
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Approximating Spherical Gaussian Mixtures
Ananda Suresh (UC San Diego)
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Discussion, Open Problems and Lucky Talk Selection
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Coffee and Check-In
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Lucky Talk: An Algorithmic Characterization of a Class of Inequalities
Gregory Valiant, Stanford University
<b>Computations for Big Data</b>
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Detection and Estimation through an Information Theory Lens
Sergio Verdú (Princeton University)
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Testing Probability Distributions using Conditional Samples
Rocco Servedio (Columbia University)
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Break
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Sample Starved Correlation Mining
Al Hero, University of Michigan
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Fast Approximations of the Pattern Maximum Likelihood Estimate
Pascal Vontobel, Chinese University of Hong Kong
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Lunch
<b>Related Topics</b>
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Spy vs Spy: Anonymous Messaging
Pramod Viswanath, University of Illinois, Urbana-Champaign
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Learning Sparse Data with Near-optimal Speed and Efficiency from a Variety of Measurement Processes
Sidharth Jaggi, Chinese University of Hong Kong
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Must One Learn the Channel to Communicate at Capacity?
Aaron Wagner, Cornell University
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Discussion and Open Problems