[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar, MC 103 (McGill), Fry Sept 14, 11:30am

Hugo Larochelle larocheh at IRO.UMontreal.CA
Mon Sep 10 10:40:22 EDT 2007


The UdeM-McGill-MITACS Machine Learning seminars are back! Note that 
this year, there will be seminars only every 2 weeks.

This week's seminar (see https://www.iro.umontreal.ca/article.php3? 
id_article=107&lang=en).

Optimal Causal Inference

by Susanna Still,
Department of Information and Computer Sciences
University of Hawaii

Location: McConnell Engineering building (McGill), room 103
Time: September 14th 2007, 11h30

I will talk about how theory building can naturally distinguish between 
regularity and randomness. Starting from basic modeling principles I 
will argue for a general information-theoretic objective function that 
embodies a trade-off between a model’s complexity and its predictive 
power. The family of solutions derived from this principle corresponds 
to a hierarchy of models. At each level of complexity, those models 
achieve maximal predictive power, and in the limit of optimal prediction 
a process’ exact causal organization is identified. Examples show how 
theory building can profit from analyzing a process’ causal 
compressibility, which is reflected in the optimal models’ 
rate-distortion curve.


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