Online Policy Learning and Inference by Matrix Completion

• 成果类型: Article; Early Access
• 署名作者: Duan, Congyuan; Li, Jingyang; Xia, Dong
• 署名单位: Hong Kong University of Science & Technology; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2537454
• 发表日期: 2025
• 关键词: guarantees; parking; rates

摘要

Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that combines an epsilon -greedy policy for decision-making with an online gradient descent algorithm for bandit parameter estimation. Our novel two-phase design balances policy learning accuracy and regret performance. For policy inference, we develop an online debiasing method based on inverse propensity weighting and establish its asymptotic normality. Our methods are applied to data from the San Francisco parking pricing project, revealing intriguing discoveries and outperforming the benchmark policy. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.