GLASSES: Relieving The Myopia Of Bayesian Optimisation

Javier GonzalezMichael OsborneNeil D. Lawrence
Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:790-799, 2016.

Abstract

We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, GLASSES, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal look-ahead loss function, which is expensive to evaluate, by a cheaper alternative in which the future steps of the algorithm are simulated beforehand. An Expectation Propagation algorithm is used to compute the expected value of the loss. We show that the far-horizon planning thus enabled leads to substantive performance gains in empirical tests.

Cite this Paper


BibTeX
@InProceedings{Gonzalez:glasses16, title = {{GLASSES}: Relieving The Myopia Of {B}ayesian Optimisation}, author = {Javier Gonzalez and Michael Osborne and Neil D. Lawrence}, booktitle = {Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics}, pages = {790--799}, year = {2016}, editor = {Arthur Gretton and Cristian Robert}, volume = {51}, address = {Cadiz, Spain}, publisher = {PMLR}, abstract = {We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, GLASSES, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal look-ahead loss function, which is expensive to evaluate, by a cheaper alternative in which the future steps of the algorithm are simulated beforehand. An Expectation Propagation algorithm is used to compute the expected value of the loss. We show that the far-horizon planning thus enabled leads to substantive performance gains in empirical tests.} }
Endnote
%0 Conference Paper %T GLASSES: Relieving The Myopia Of Bayesian Optimisation %A Javier Gonzalez %A Michael Osborne %A Neil D. Lawrence %B Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics %D 2016 %E Arthur Gretton %E Cristian Robert %F Gonzalez:glasses16 %I PMLR %P 790--799 %V 51 %X We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, GLASSES, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal look-ahead loss function, which is expensive to evaluate, by a cheaper alternative in which the future steps of the algorithm are simulated beforehand. An Expectation Propagation algorithm is used to compute the expected value of the loss. We show that the far-horizon planning thus enabled leads to substantive performance gains in empirical tests.
RIS
TY - CPAPER TI - GLASSES: Relieving The Myopia Of Bayesian Optimisation AU - Javier Gonzalez AU - Michael Osborne AU - Neil D. Lawrence BT - Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics DA - 2016/05/02 ED - Arthur Gretton ED - Cristian Robert ID - Gonzalez:glasses16 PB - PMLR VL - 51 SP - 790 EP - 799 AB - We present GLASSES: Global optimisation with Look-Ahead through Stochastic Simulation and Expected-loss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the non-myopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, GLASSES, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal look-ahead loss function, which is expensive to evaluate, by a cheaper alternative in which the future steps of the algorithm are simulated beforehand. An Expectation Propagation algorithm is used to compute the expected value of the loss. We show that the far-horizon planning thus enabled leads to substantive performance gains in empirical tests. ER -
APA
Gonzalez, J., Osborne, M. & Lawrence, N.D.. (2016). GLASSES: Relieving The Myopia Of Bayesian Optimisation. Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics 51:790-799

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