Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
Ouyang, Jiangrong Gong, Mingming Bondell, Howard
Ouyang, Jiangrong
Gong, Mingming
Bondell, Howard
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Policy inference plays an essential role in the contextual bandit problem. In this paper, we use empirical likelihood to develop a Bayesian inference method for the joint analysis of multiple contextual bandit policies in finite sample regimes. The proposed inference method is robust to small sample sizes and is able to provide accurate uncertainty measurements for policy value evaluation. In addition, it allows for flexible inferences on policy comparison with full uncertainty quantification. We demonstrate the effectiveness of the proposed inference method using Monte Carlo simulations and its application to an adolescent body mass index data set.
Citation
J. Ouyang, M. Gong, H. Bondell, "Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood," Journal of Machine Learning Research, vol. 27, 2026,
Source
Journal of Machine Learning Research
Conference
Keywords
Subjects
Source
Publisher
Microtome Publishing