\dm_csml_event_details UCL ELLIS

Batch Bayesian Optimization via Local Penalization


Javier Gonzalez


University of Sheffield


Friday, 29 May 2015






Roberts G08

Event series

DeepMind/ELLIS CSML Seminar Series


The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimization of functions. However, most proposed approaches only allow the exploration of the parameter space to occur sequentially. Often, it is desirable to simultaneously propose batches of parameter values to explore. This is particularly the case when large parallel processing facilities are available, which can be either computational or physical facets of the process being optimized. Batch methods, however, require modelling of the interaction between the evaluations in the batch, which can be expensive in complex scenarios. We investigate this issue and we propose a simple heuristic based on an estimate of the function Lipschitz's constant that captures the most important aspect of this interaction, i.e., local repulsion, at negligible computational overhead. The resulting algorithm compares well, in running time, with much more elaborate alternatives. A penalized acquisition function is used to collect batches of points of certain size minimizing the non-parallelizable computational effort. The speed-up of our method with respect to previous approaches is significant in a set of computationally expensive experiments.