Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2023
Abstract
Bayesian Optimization (BO) has recently received increasing attention due to its efficiency in optimizing expensive-to-evaluate functions. For some practical problems, it is essential to consider the path-dependent switching cost between consecutive sampling locations given a total traveling budget. For example, when using a drone to locate cracks in a building wall or search for lost survivors in the wild, the search path needs to be efficiently planned given the limited battery power of the drone. Tackling such problems requires a careful cost-benefit analysis of candidate locations and balancing exploration and exploitation. In this work, we formulate such a problem as a constrained Markov Decision Process (MDP) and solve it by proposing a new distance-adjusted multi-step look-ahead acquisition function, the distUCB, and using rollout approximation. We also provide a theoretical regret analysis of the distUCB-based Bayesian optimization algorithm. In addition, the empirical performance of the proposed algorithm is tested based on both synthetic and real data experiments, and it shows that our cost-aware non-myopic algorithm performs better than other popular alternatives.
Keywords
Machine Learning, Bayesian learning, Hyperparameter optimization
Discipline
Analysis | Finance and Financial Management | Operations and Supply Chain Management
Research Areas
Quantitative Finance
Publication
Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023: Macao, August 19-25
First Page
4011
Last Page
4018
ISBN
9781956792034
Identifier
10.24963/ijcai.2023/446
Publisher
AAAI Press
City or Country
Washington, DC
Citation
LIU, Peng; WANG, Haowei; and QIYU, Wei.
Bayesian optimization with switching cost: Regret analysis and lookahead variants. (2023). Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023: Macao, August 19-25. 4011-4018.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7257
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.24963/ijcai.2023/446
Included in
Analysis Commons, Finance and Financial Management Commons, Operations and Supply Chain Management Commons