arXiv Machine Learning By Shafkat Farabi, Didac Marti Pinto, Wei Lu, Manuel Ramos-Maqueda, Sanmay Das, Antoine Deeb, Anja Sautmann

SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary

Read the original on arXiv Machine Learning →

arXiv:2602. 18431v2 Announce Type: replace-cross Abstract: Motivated by the problem of assigning mediators to cases in the Kenyan judicial system, we study an online resource allocation problem where incoming tasks (cases) must be immediately assigned to available, capacity-constrained resources (mediators).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
1d ago

Sequential Batch Learning in Finite-Action Linear Contextual Bandits

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 Machine Learning
6d ago

When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits

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