Saturday Morning Videos: Information Theory, Learning and Big Data @ Simons Institute, Berkeley

 
Here are the videos and slides for the Information Theory, Learning and Big Data @ Simons Institute, Berkeley

Sparsity and Base Learning

• Low-Rank Matrix Recovery Through Rank-One Projections, Tony Cai, University of Pennsylvania
• Sketching for M-Estimators: A Unified Approach to Robust Regression ,  David Woodruff, IBM Almaden
• Simple, Efficient and Neural Algorithms for Sparse Coding ,  Ankur Moitra, Massachusetts Institute of Technology
• Learning a Hidden Basis through Imperfect Measurements: Why and How,  Misha Belkin, Ohio State University

Clustering and Community Organization

• Constrained Rank Aggregation and Correlation Clustering, Olgica Milenkovic, University of Illinois, Urbana-Champaign
• Testing Distribution Families, Jayadev Acharya, Massachusetts Institute of Technology
• Fundamental Limit of Community Detection in Stochastic Block Models, Emmanuel Abbe, Princeton University
• How Large is the Norm of a Random Matrix?,  Ramon van Handel, Princeton University
• Nearly Linear-Time Algorithms for Structured Sparsity, Piotr Indyk, Massachusetts Institute of Technology
• Lucky Talk: From Coding to Clustering,  Emmanuel Abbe, Princeton University

Distribution Properties

• Chebyshev Polynomials, Moment Matching and Optimal Estimation of the Unseen, Yihong Wu, University of Illinois, Urbana-Champaign
• Information Measure Estimation and Applications: Boosting the Sample Size from n to n log n, Jiantao Jiao, Stanford University
• Sample Complexity of Estimating Entropy,  Himanshu Tyagi, Indian Institute of Science
• Minimum Rényi Correlation Principle: From Marginals to Joint Distribution, Farzan Farnia, Stanford University

Sequential Estimation and Compression

• Adaptive Coding over Countable Alphabets,  Stéphane Boucheron, Université Paris Diderot
• On Learning Distributions from their Samples, Sudeep Kamath, Princeton University
• Efficient Minimax Optimal Strategies for Universal Prediction, Peter Bartlett, UC Berkeley
• On the Complexity of Best Arm Identification in Multi-Armed Bandit Models, Aurélien Garivier, University of Toulouse
• Lucky Talk: Chebyshev Polynomials, Moment Matching and Optimal Estimation of the Unseen, Yihong Wu, University of Illinois, Urbana-Champaign

Information Aggregation and Pattern Matching

• Low Regret Recommendations and Item-Item Collaborative Filtering, Devavrat Shah, Massachusetts Institute of Technology
• Topic Modeling Approach for Rank Aggregation,  Venkat Saligrama, Boston University
• Machine Learning from Human Comparative Judgments,  Robert Nowak, University of Wisconsin-Madison
• Analytic Pattern Matching: From DNA to Twitter,  Wojciech Szpankowski, Purdue University

Learning and Information Theory

• Strong Data Processing Inequalities and Estimation with Constraints, John Duchi, Stanford University
• Strong Data Processing Inequalities: Applications to MCMC and Graphical Models, Maxim Raginsky, University of Illinois, Urbana-Champaign
• Near-Optimal Hypothesis Testing via Convex Optimization,  Arkadi Nemirovski, Georgia Institute of Technology
• Data Driven Convergence in Statistical Estimators, Narayana Santhanam, University of Hawaii
• Lucky Talk: Low-regret Recommendations and Item-item Collaborative Filtering, Guy Bresler, Massachusetts Institute of Technology for Devavrat Shah, Massachusetts Institute of Technology

Optimization and Learning Models

• Confidence-Based Active Learning, Kamalika Chaudhuri, UC San Diego
• Constructing Informative Features for Discriminative Learning, Animashree Anandkumar, UC Irvine
• Robust Inference and Local Algorithms,  Yishay Mansour, Tel Aviv University
• Tradeoffs in Large Scale Learning: Statistical Accuracy vs. Numerical Precision, Sham Kakade, Microsoft Research New England
• Computation-Statistics Tradeoffs in Unsupervised Learning via Data Summarization, Mesrob Ohannessian, UC San Diego

Learning Structured Distributions

• Statistical Property Testing and Estimation Beyond the i.i.d. Setting, Gregory Valiant, Stanford University
• Nearest Neighbor Based Greedy Coordinate Descent,  Pradeep Ravikumar, University of Texas, Austin
• Learning and Testing Structured Distributions,  Ilias Diakonikolas, University of Edinburgh
• Approximating Spherical Gaussian Mixtures,  Ananda Suresh, UC San Diego
• Lucky Talk: An Algorithmic Characterization of a Class of Inequalities, Gregory Valiant, Stanford University

Computations for Big Data

• Detection and Estimation through an Information Theory Lens,  Sergio Verdú, Princeton University
• Testing Probability Distributions using Conditional Samples, Rocco Servedio, Columbia University
• Sample Starved Correlation Mining, Al Hero, University of Michigan
• Fast Approximations of the Pattern Maximum Likelihood Estimate, Pascal Vontobel, Chinese University of Hong Kong
• Spy vs Spy: Anonymous Messaging, Pramod Viswanath, University of Illinois, Urbana-Champaign
• Learning Sparse Data with Near-optimal Speed and Efficiency from a Variety of Measurement Processes, Sidharth Jaggi, Chinese University of Hong Kong
• Must One Learn the Channel to Communicate at Capacity?, Aaron Wagner, Cornell University

 

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