Playlist: 44 videos Workshops

Information Theory, Learning and Big Data

Mar. 16 – Mar. 20, 2015

The three terms in the title of the workshop are three facets of the same basic question: what information can be gleaned from observed data? In information theory, especially in universal compression, observed data is used...

0:26:39

Information Measure Estimation and Applications: Boosting the Sample Size from n to n log n

Jiantao Jiao, Stanford University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/jiantao-jiao-2015-03-17
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0:28:0

Sample Complexity of Estimating Entropy

Himanshu Tyagi, Indian Institute of Science
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/himanshu-tyagi-2015-03-17
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0:21:17

Minimum Rényi Correlation Principle: From Marginals to Joint Distribution

Farzan Farnia, Stanford University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/farzan-farnia-2015-03-17
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0:26:20

Adaptive Coding over Countable Alphabets

Stéphane Boucheron, Université Paris Diderot
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/stephane-boucheron-2015-03-17
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0:29:2

On Learning Distributions from their Samples

Sudeep Kamath, Princeton University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/sudeep-kamath-2015-03-17
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0:28:34

Efficient Minimax Optimal Strategies for Universal Prediction

Peter Bartlett, UC Berkeley
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/peter-bartlett-2015-03-17
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0:26:58

On the Complexity of Best Arm Identification in Multi-Armed Bandit Models

Aurélien Garivier, University of Toulouse
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/aurelien-garivier-2015-03-17
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0:21:15

Lucky Talk: Chebyshev Polynomials, Moment Matching and Optimal Estimation of the Unseen

Yihong Wu, University of Illinois, Urbana‑Champaign
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/yihong-wu-2015-03-18
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0:25:26

Low Regret Recommendations and Item-Item Collaborative Filtering

Devavrat Shah, Massachusetts Institute of Technology
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/devavrat-shah-2015-03-18
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0:30:31

Topic Modeling Approach for Rank Aggregation

Venkat Saligrama, Boston University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/venkat-saligrama-2015-03-18
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