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MCMC Learning

Published on Aug 20, 20152056 Views

Varun Kanade

The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas. This theo

COLT 2015 - Paris

Related categories

Active LearningComputational Learning TheoryOn-line LearningReinforcement LearningSemi-supervised Learning

Chapter list

MCMC Learning00:00
PAC Learning00:06
Agnostic Learning01:02
Challenge01:59
Uniform Distribution Learning02:26
But ...04:12
Markov Random Fields - 104:44
Markov Random Fields - 205:36
Learning Model (PAC/Agnostic)06:28
Gibbs Sampling (MCMC Algorithm)07:09
Ising Model07:41
Harmonic Analysis Using Eigenvectors - 108:40
Harmonic Analysis Using Eigenvectors - 209:43
Power Iteration to Find Eigenvectors - 110:13
Power Iteration to Find Eigenvectors - 211:25
When can we do this?12:18
Discrete Spectrum13:24
Learning Algorithm13:59
Main Results15:26
Open Questions17:00