[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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