arXiv Machine Learning By Ibne Farabi Shihab, Joyanta Jyoti Mondal, Anuj Sharma

Discrepancy-Rounded Fair Bandits with Static and Time-Varying Exposure Floors

Read the original on arXiv Machine Learning →

arXiv:2607. 22935v1 Announce Type: new Abstract: Minimum-exposure constraints arise in recommendation, content curation, and regulated allocation when each provider, arm, or group must receive guaranteed exposure inside a period rather than only in aggregate.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 4

Meritocratic Fairness via $K$-Shapley Values in Budgeted Combinatorial Bandits with Full-Bandit Feedback

arXiv:2605. 00762v2 Announce Type: replace Abstract: We study meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback, where a learner selects at most $K$ arms per time step and observes only the noisy aggregate reward of the selected set.

By Shradha Sharma, Shweta Jain, Swapnil Dhamal