Document detail
ID
oai:arXiv.org:2410.14533
Topic
Statistics - Methodology Computer Science - Machine Learnin...Author
Chen, Qiyuan Kontar, Raed AlCategory
Computer Science
Year
2024
listing date
10/23/2024
Keywords
framework costs movementAbstract
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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