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The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs

arXiv 2410.14533 ↗First posted 18 Oct 2024stat.ME cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 18 Oct 2024.

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22 of 58 references.

References · 22

  1. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  2. 2017On Kernelized Multi-armed Banditscited by 348
  3. 2019Batched Multi-armed Bandits Problemcited by 97
  4. 2013Online Learning with Switching Costs and Other Adaptive Adversariescited by 60
  5. 2021Gaussian Process Bandit Optimization with Few Batchescited by 57
  6. 2013Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Explorationcited by 50
  7. 2019Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisationcited by 47
  8. 2020Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Treescited by 41
  9. 2013Bandits with Switching Costs: T^{2/3} Regretcited by 38
  10. 2015GLASSES: Relieving The Myopia Of Bayesian Optimisationcited by 38
  11. 2019Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rolloutcited by 23
  12. 2021Lipschitz Bandits with Batched Feedbackcited by 21
10 more
  1. 2021An Algorithm for Stochastic and Adversarial Bandits with Switching Costscited by 18
  2. 2021A Nonmyopic Approach to Cost-Constrained Bayesian Optimizationcited by 18
  3. 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Indexcited by 16
  4. 2022SnAKe: Bayesian Optimization with Pathwise Explorationcited by 14
  5. 2019On Batch Bayesian Optimizationcited by 12
  6. 2022Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Timescited by 12
  7. 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
  8. 2017Bandits with Movement Costs and Adaptive Pricingcited by 6
  9. 2017Multi-Armed Bandits with Metric Movement Costscited by 6
  10. 2022Bayesian Optimisation for Active Monitoring of Air Pollutioncited by 5

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