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...

0:32:49

Tradeoffs in Large Scale Learning: Statistical Accuracy vs. Numerical Precision

Sham Kakade, Microsoft Research New England
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/sham-kakade-2015-03-19
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0:32:36

Computation-Statistics Tradeoffs in Unsupervised Learning via Data Summarization

Mesrob Ohannessian, UC San Diego
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/mesrob-ohannessian-2015-03-19
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0:30:1

Statistical Property Testing and Estimation Beyond the i.i.d. Setting

Gregory Valiant, Stanford University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/gregory-valiant-2015-03-19
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0:30:41

Nearest Neighbor Based Greedy Coordinate Descent

Pradeep Ravikumar, University of Texas, Austin
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/pradeep-ravikumar-2015-03-19
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0:33:10

Learning and Testing Structured Distributions

Ilias Diakonikolas, University of Edinburgh
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/ilias-diakonikolas-2015-03-19
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0:28:35

Approximating Spherical Gaussian Mixtures

Ananda Suresh, UC San Diego
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/ananda-suresh-2015-03-19
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0:23:26

Lucky Talk: An Algorithmic Characterization of a Class of Inequalities

Gregory Valiant, Stanford University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/gregory-valiant-2015-03-20
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0:26:54

Detection and Estimation through an Information Theory Lens

Sergio Verdú, Princeton University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/sergio-verdu-2015-03-20
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0:29:18

Testing Probability Distributions using Conditional Samples

Rocco Servedio, Columbia University
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/rocco-servedio-2015-03-20
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0:27:53

Sample Starved Correlation Mining

Al Hero, University of Michigan
Information Theory, Learning and Big Data
http://simons.berkeley.edu/talks/al-hero-2015-03-20
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