NIPS Workshop on Representations and Inference on Probability Distributions, Whistler 2007

NIPS Workshop on Representations and Inference on Probability Distributions, Whistler 2007

9 Lectures · Dec 8, 2007

About

When dealing with distributions it is in general infeasible to estimate them explicitly in high dimensional settings, since the associated learning rates can be arbitrarily slow. On the other hand, a great variety of applications in machine learning and computer science require distribution estimation and/or comparison. Examples include testing for homogeneity (the "two-sample problem"), independence, and conditional independence, where the last two can be used to infer causality; data set squashing / data sketching / data anonymisation; domain adaptation (the transfer of knowledge learned on one domain to solving problems on another, related domain) and the related problem of covariate shift; message passing in graphical models (EP and related algorithms); compressed sensing; and links between divergence measures and loss functions.

The purpose of this workshop is to bring together statisticians, machine learning researchers, and computer scientists working on representations of distributions for various inference and testing problems, to discuss the compromises necessary in obtaining useful results from finite data. In particular, what are the capabilities and weaknesses of different distribution estimates and comparison strategies, and what negative results apply?

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Uploaded videos:

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02:10

Introduction to the Workshop

Arthur Gretton

Feb 25, 2008

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2793 Views

Lecture
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42:27

Testing Distributions for Goodness of fit, Homogeneity, and Independence

Tugkan Batu

Feb 25, 2008

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4146 Views

Lecture
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33:46

On a L1-Test Statistic of Homogeneity

Gérard Biau

Feb 25, 2008

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3912 Views

Lecture
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24:31

Adaptive Representations for Efficient Inference for Distributions on Permutatio...

Carlos Guestrin

Feb 25, 2008

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3575 Views

Lecture
13:55

A Framework for Probability Density Estimation

Shai Ben-David

Feb 25, 2008

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4433 Views

Lecture
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08:43

Kullback-Leibler Divergence Estimation of Continuous Distributions

Fernando Perez-Cruz

Feb 25, 2008

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9847 Views

Lecture
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14:28

Testing with Kernel-based Test Statistics Power Against Sequences of Local Alter...

Zaid Harchaoui

Feb 25, 2008

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3466 Views

Lecture
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45:46

Subjective Measure for Distribution Similarity

Shai Ben-David

Feb 25, 2008

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3982 Views

Lecture
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37:36

Sketching and Streaming for Distributions

Andrew McGregor

Feb 25, 2008

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3067 Views

Lecture