[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Fri Dec. 3 at 10h30, AA-3195

Dumitru Erhan erhandum at iro.umontreal.ca
Lun 29 Nov 15:17:12 EST 2010


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.
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