Original paper
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes
Abstract
Bayesian Optimization (BO) methods are useful for optimizing functions that are expen- sive to evaluate, lack an analytical expression and whose evaluations can be contaminated by noise. These methods rely on a probabilistic model of the objective function, typically a Gaussian process (GP), upon which an acquisition function is built. The acquisition function guides the optimization process and measures the expected utility of performing an...
Paper Details
Title
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes
Published Date
Mar 1, 2020
Journal
Volume
380
Pages
20 - 35
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Notes
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