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Preprints, Working Papers, ... Year : 2023

A portfolio approach to massively parallel Bayesian optimization

Abstract

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

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Dates and versions

hal-03383097 , version 1 (18-10-2021)
hal-03383097 , version 2 (31-05-2022)
hal-03383097 , version 3 (03-04-2023)

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Mickael Binois, Nicholson Collier, Jonathan Ozik. A portfolio approach to massively parallel Bayesian optimization. 2023. ⟨hal-03383097v3⟩
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