Jose Blanchet, Renyuan Xu, Zhengyuan Zhou (2023) Delay-Adaptive Learning in Generalized Linear Contextual Bandits. Mathematics of Operations Research 49(1):326-345.
https://doi.org/10.1287/moor.2023.1358
Abstract
In this paper, we consider online learning in generalized linear contextual bandits where rewards are not immediately observed. Instead, rewards are available to the decision maker only after some delay, which is unknown and stochastic. Such delayed feedback occurs in several active learning settings, including product recommendation, personalized medical treatment selection, bidding in first-price auctions, and bond trading in over-the-counter markets. We study the performance of two well-known algorithms adapted to this delayed setting: one based on upper confidence bounds and the other based on Thompson sampling. We describe modifications on how these two algorithms should be adapted to handle delays and give regret characterizations for both algorithms. To the best of our knowledge, our regret bounds provide the first theoretical characterizations in generalized linear contextual bandits with …
Authors
Jose Blanchet, Renyuan Xu, Zhengyuan Zhou
Publication date
2024/2
Journal
Mathematics of Operations Research
Volume
49
Issue
1
Pages
326-345
Publisher
INFORMS
