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

Participants: