arXiv:2608. 11560v1 Announce Type: new Abstract: Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning.
By Sang Su Lee, Vineeth Loganathan, Shishir Dash, Vijay Raghavan
arXiv:2004. 06321v2 Announce Type: replace Abstract: We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.
By Yanjun Han, Zhengqing Zhou, Zihao Hu, Jose Blanchet, Peter W. Glynn, Yinyu Ye, Zhengyuan Zhou
Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning. Teams therefore train the bandit on a fast proxy reward, and separately must judge whether a contextual bandit is worth its complexity over sending one best message.
The paper introduces Budget-Constrained Causal Bandits (BCCB), an online framework that learns individual treatment effects, explores uncertain users, and manages budget pacing simultaneously. It derives a per-arrival decision rule from a KKT condition of a Lagrangian relaxation, providing a principled algorithmic foundation. Experiments on the Criteo Uplift dataset show BCCB outperforms offline pipelines and other online baselines, especially when historical data is scarce (below 7,500 observations).
By Abhirami Pillai
arXiv:2307. 03587v4 Announce Type: replace Abstract: In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator.
By Nicklas Werge, Yi-Shan Wu, Abdullah Akg\"ul, Melih Kandemir
arXiv:2606. 09802v1 Announce Type: cross Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time.
By Udvas Das, Waris Radji, Debabrota Basu, Odalric-Ambrym Maillard