---------- Forwarded message ----------
From: Dumitru Erhan <erhandum@iro.umontreal.ca>
Date: Mon, Nov 29, 2010 at 15:17
Subject: UdeM-McGill-MITACS machine learning seminar Fri Dec. 3@10h30, AA-3195
To: lisa_seminaires@iro.umontreal.ca

The UdeM-McGill-MITACS machine learning seminar series is continuing its fall schedule. Friday's seminar:

Adaptive stochastic search: tuning Gaussians' covariances

by Rémi Bardenet and Djalel Benbouzid
University Paris-Sud XI

Location: Pavillon André-Aisenstadt (UdeM), room AA-3195
Time: Friday, December 3, 10:30 (!)

Abstract: Stochastic search algorithms are ubiquitous in optimization and statistics: they are at the core of some of the most efficient blackbox optimization techniques (e.g., Evolutionary Strategies), and of great practical use in Bayesian inference (e.g., in Metropolis-Hastings algorithms). In this talk I will concentrate on stochastic search techniques with Gaussian proposals that learn from their exploration to tune their covariance. After a quick overview of the field and methods, I will present 1) an evolutionary optimization algorithm with mixture proposals and its application to a Bayesian optimization problem, and 2) a novel attempt-in-progress at defining nonlinear adaptive Gaussian proposals. The first item deals with Gaussian Process based surrogate optimization: when the function to optimize is costly to evaluate (e.g., hyperparameter optimization in Machine Learning), one often relies on a surrogate model that is learnt on the fly, but optimizing this auxiliary surrogate can be a difficult task itself. The second problem is about mixing good properties of adaptive MCMC with reproducing kernel Hilbert spaces to exploit nonlinearity in the data.