Towards Practical Lipschitz Bandits

Conference Paper

Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains. In this paper, we present a framework for Lipschitz bandit methods that adaptively learns partitions of context-and arm-space. Due to this flexibility, the algorithm is able to efficiently optimize rewards and minimize regret, by focusing on the portions of the space that are most relevant. In our analysis, we link tree-based methods to Gaussian processes. In light of our analysis, we design a novel hierarchical Bayesian model for Lipschitz bandit problems. Our experiments show that our algorithms can achieve state-of-the-art performance in challenging real-world tasks such as neural network hyperparameter tuning.

Full Text

Duke Authors

Cited Authors

  • Wang, T; Ye, W; Geng, D; Rudin, C

Published Date

  • October 19, 2020

Published In

  • Fods 2020 Proceedings of the 2020 Acm Ims Foundations of Data Science Conference

Start / End Page

  • 129 - 138

International Standard Book Number 13 (ISBN-13)

  • 9781450381031

Digital Object Identifier (DOI)

  • 10.1145/3412815.3416885

Citation Source

  • Scopus