Bayesian optimization is a sample efficient sequential global optimization method for black-box, expensive and multi-extremal functions. It generates, and keeps updated, a probabilistic surrogate model of the objective function, depending on the performed evaluations, and optimizes an acquisition function to choose a new point to evaluate. The acquisition function deals with the exploration-exploitation dilemma depending on surrogate's predictive mean and uncertainty. Many alternatives are available offering different trade-off mechanisms; different options are also possible for the probabilistic surrogate model: Gaussian Process regression is best suited for optimization over continuous search spaces while other approaches, such as Random Forests or Gaussian Prcesses with ah-hoc kernels, deal with complex search spaces spanned by nominal, numeric and conditional variables. This tutorial offers an introduction to these topics and a discussion on available tools, real-life applications, and recent advances, such as unknown constraints, multi-information sources and cost-awareness, and multi-objective optimization.

Candelieri, A. (2021). A Gentle Introduction to Bayesian Optimization. In Proceedings - Winter Simulation Conference (pp.1-16). Institute of Electrical and Electronics Engineers Inc. [10.1109/WSC52266.2021.9715413].

A Gentle Introduction to Bayesian Optimization

Candelieri A.
2021

Abstract

Bayesian optimization is a sample efficient sequential global optimization method for black-box, expensive and multi-extremal functions. It generates, and keeps updated, a probabilistic surrogate model of the objective function, depending on the performed evaluations, and optimizes an acquisition function to choose a new point to evaluate. The acquisition function deals with the exploration-exploitation dilemma depending on surrogate's predictive mean and uncertainty. Many alternatives are available offering different trade-off mechanisms; different options are also possible for the probabilistic surrogate model: Gaussian Process regression is best suited for optimization over continuous search spaces while other approaches, such as Random Forests or Gaussian Prcesses with ah-hoc kernels, deal with complex search spaces spanned by nominal, numeric and conditional variables. This tutorial offers an introduction to these topics and a discussion on available tools, real-life applications, and recent advances, such as unknown constraints, multi-information sources and cost-awareness, and multi-objective optimization.
No
slide + paper
Bayesian Optimization
English
2021 Winter Simulation Conference, WSC 2021 - 12 December 2021 through 15 December 2021
9781665433112
Candelieri, A. (2021). A Gentle Introduction to Bayesian Optimization. In Proceedings - Winter Simulation Conference (pp.1-16). Institute of Electrical and Electronics Engineers Inc. [10.1109/WSC52266.2021.9715413].
Candelieri, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/396703
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