An Entropy Search Portfolio for Bayesian Optimization

By Bobak Shahriari, Ziyun Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas. 2014

62 citations recorded by the source corpus.

Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i.e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance. While portfolio methods provide an effective, principled way of combining a collection of acquisition functions, they are often based on measures of past performance which can be misleading. To address this issue, we introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio construction which is motivated by information theoretic considerations. We show that ESP outperforms existing portfolio methods on several real and synthetic problems, including geostatistical datasets and simulated control tasks. We not only show that ESP is able to offer performance as good as the best, but unknown, acquisition function, but surprisingly it often gives better performance. Finally, over a wide range of conditions we find that ESP is robust to the inclusion of poor acquisition functions.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

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Citations in the corpus

Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, methodology): “An entropy search portfolio (Shahriari et al., 2014) selects the acquisition function using the information gained toward optimization.”
Metacontrol for Adaptive Imagination-Based Optimization (2017, background): “B depicts an easy, 1-planet scene, while C depicts a very difﬁcult 5-planet scene. several basic selection criteria Hoffman et al. (2011); Shahriari et al. (2014).”
Parallelised Bayesian Optimisation via Thompson Sampling (2018, background): “TS has been explored for sequential BO [4, 39] and some recent theoretical advances have characterised the performance of TS in sequential settings [3, 7, 27, 35, 36].”
Heteroscedastic Treed Bayesian Optimisation (2014, background): “The expected improvement is best understood as a family of one-step-decision heuristics Brochu et al. (2009), with many members in this family.”
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space (2017, methodology): “While there are BO methods that allow parallel data collection, these methods have typically been limited to tens of data points per batch (Snoek et al., 2012; Shahriari et al., 2014; Gonzlez et al., 2016).”