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  • title: Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
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            Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
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            Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

            Nov 28, 2022

            Speakers

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            Samuel Daulton

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            XW

            Xingchen Wan

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            DE

            David Eriksson

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            About

            Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications. Bayesian optimization (BO) is a popular sample-efficient method that selects promising designs to evaluate by optimizing an acquisition function (AF) over some domain with respect to a surrogate model. However, maximizing the AF over mixed or high-cardinality discrete search spaces is challenging as we cannot use stand…

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            NeurIPS 2022

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