Bayesian Optimization of Multiple Objectives with Different Latencies

Autor:	Buckingham, Jack M., Gonzalez, Sebastian Rojas, Branke, Juergen
Rok vydání:	2023
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Statistics - Machine Learning Optimization and Control (math.OC) FOS: Mathematics Machine Learning (stat.ML) Mathematics - Optimization and Control Machine Learning (cs.LG)
DOI:	10.48550/arxiv.2302.01310
Popis:	Multi-objective Bayesian optimization aims to find the Pareto front of optimal trade-offs between a set of expensive objectives while collecting as few samples as possible. In some cases, it is possible to evaluate the objectives separately, and a different latency or evaluation cost can be associated with each objective. This presents an opportunity to learn the Pareto front faster by evaluating the cheaper objectives more frequently. We propose a scalarization based knowledge gradient acquisition function which accounts for the different evaluation costs of the objectives. We prove consistency of the algorithm and show empirically that it significantly outperforms a benchmark algorithm which always evaluates both objectives. Comment: 25 pages
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7606f30557b50d31b68208ecef5747c8 Zobrazit plný text záznamu