Wednesday, November 20, 2013
4:00 pm
Annenberg 213

Special Seminar in Applied Mathematics

Learning the Learning Rate: How to Repair Bayes When the Model is Wrong
Professor Peter Gr├╝nwald, CWI Amsterdam & Leiden University


Bayesian inference can behave badly if the model under consideration is wrong yet useful: the posterior may fail to concentrate even for large samples, leading to extreme overfitting in practice. We introduce a test that can tell from the data whether we are heading for such a situation. If we are, we adjust the learning rate (equivalently: make the prior lighter-tailed, or penalize the likelihood more) in a data-dependent way. The resulting "safe" estimator continues to achieve good rates with wrong models. In classification problems, it learns faster in easy settings, i.e. when a Tsybakov condition holds. The safe estimator is based on empirical mixability, which generalizes an idea from worst-case online prediction. Thus, safe estimation connects three paradigms: Bayesian inference, (frequentist) statistical learning theory and (worst-case) on-line prediction.

* For an informal introduction to the idea, see Larry Wasserman's blog
http://normaldeviate.wordpress.com/2012/06/26/self-repairing-bayesian-inference/

Contact Carmen Nemer-Sirois carmens@caltech.edu at 4561
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