Statistical Inference for Online Decision Making: In a Contextual Bandit Setting-Reference-Cited by-同舟云学术

Statistical Inference for Online Decision Making: In a Contextual Bandit Setting

Published:2020-07-07 Issue:533 Volume:116 Page:240-255
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Chen Haoyu¹,Lu Wenbin¹,Song Rui¹

Affiliation:

1. Department of Statistics, North Carolina State University, Raleigh, NC

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2020.1770098

Reference29 articles.

1. Agrawal, S., and Goyal, N. (2013), “Thompson Sampling for Contextual Bandits With Linear Payoffs,” in International Conference on Machine Learning, pp. 127–135.

2. The Search for Optimality in Clinical Trials

3. Online Decision Making with High-Dimensional Covariates

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Anytime-valid off-policy inference for contextual bandits;ACM / IMS Journal of Data Science;2024-01-31

2. Online Regularization toward Always-Valid High-Dimensional Dynamic Pricing;Journal of the American Statistical Association;2023-11-17

3. Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning;Journal of the American Statistical Association;2023-11-08

4. Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection;Journal of the American Statistical Association;2022-09-27

5. Optimal strategy for Bayesian two-armed bandit problem with an arched reward function;Mathematical Control and Related Fields;2022