The UdeM-McGill-MITACS Machine Learning seminars are back! Note that this year, there will be seminars only every 2 weeks. The seminar will be in room 437, NOT 103! 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 437 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.