AIM 2011 Singular Learning Theory slides

Semester: Fall 2011
Organizers: Russel Steele, Bernd Sturmfels, and Sumio Watanabe
Location: American Institute of Mathematics, Palo Alto, California
Date: Dec 12 – 16, 2011

This page hosts a collection of slides, links, and other resources relevant to the 2011 AIM workshop on Singular Learning Theory.

Day Speaker Title, slides and links
Monday Mathias Drton Reduced Rank Regression
Monday Shaowei Lin Singular Learning Theory – a view from algebraic geometry
see also: Asymptotic approximation of marginal likelihood ratios
Tuesday Anton Leykin Computational algebraic geometry – Learning coefficients via symbolic and numerical methods
see also: a short Macaulay2 demonstration
Tuesday Sumio Watanabe Algebraic geometry and model selection
Wednesday Franz Kiraly Approximate algebra for parametric estimateion
Wednesday Helene Massam The geometry of discrete loglinear models and Bayes factors
Thursday Martyn Plummer Bayesian model selection
Thursday Miki Aoyagi Consideration on singularities in learning theory and real log canonical tresholds
Friday Piotr Zwiernik Asymptotic behaviour of the marginal likelihood integral for general Markov models
Friday Alexander Schliep Model selection in bioinformatics: three short stories