Documentdetail
ID kaart
oai:arXiv.org:2410.14533
Onderwerp
Statistics - Methodology Computer Science - Machine Learnin...Auteur
Chen, Qiyuan Kontar, Raed AlCategorie
Computer Science
Jaar
2024
vermelding datum
23-10-2024
Trefwoorden
framework costs movementBeschrijving
This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs.
Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed following the solution of a Traveling Salesman Problem.
The proposed method provides a theoretical guarantee of convergence in terms of movement costs for BO.
Empirically, our method effectively reduces average movement costs over time while maintaining comparable regret performance to conventional BO methods.
This framework also shows promise for broader applications in various bandit settings with movement costs.
Chen, Qiyuan,Kontar, Raed Al, 2024, The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs
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