arXiv:2607. 13686v1 Announce Type: new Abstract: We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation.
By Hao Qin, Chicheng Zhang
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
arXiv:2602. 09456v2 Announce Type: replace Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward function approximation to offline regression.
By Hao Qin, Chicheng Zhang
arXiv:2510. 21431v2 Announce Type: replace-cross Abstract: We study the combinatorial semi-bandit problem where an agent selects a subset of base arms and receives individual feedback.
By Jung-hun Kim, Milan Vojnovi\'c, Min-hwan Oh
arXiv:2606. 31449v1 Announce Type: new Abstract: We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity.
By Tanmay Goyal, Sukruta Prakash Midigeshi, Gaurav Sinha
arXiv:2605. 09454v2 Announce Type: replace-cross Abstract: We study the $\textit{single-index bandit}$ problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an unknown reward function.
By Devdan Dey, Sujoy Bhore, Avishek Ghosh