arXiv Machine Learning June 16, 2026 By Maheed H. Ahmed, Mahsa Ghasemi

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

Read the original on arXiv Machine Learning →

arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.

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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

reinforcement-learning safety

arXiv Machine Learning

Sep 22

Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Bandits

arXiv:2602.04125v2 Announce Type: replace-cross Abstract: Modern digital platforms use contextual bandits to allocate valuable exposure and opportunities among competing participants. Fair treatment...

By Qingwen Zhang, Wenjia Wang

safety

arXiv Machine Learning

Jun 19

Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning

arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.

By Ananya Kunisetty, Avishek Ghosh

agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 2

Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits

arXiv:2606. 01799v1 Announce Type: new Abstract: We study $N$-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identification (BAI), weak regret, and strong regret.

By Pu Wang, Yao-Xiang Ding

arXiv Machine Learning

Jun 30

Optimal Regret for Single Index Bandits

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

reinforcement-learning

arXiv Machine Learning

23h ago

Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness

arXiv:2610. 01377v1 Announce Type: new Abstract: We study causal logistic bandits with counterfactual fairness constraints.

By Junhyuk Huh, Seoungbin Bae, Dabeen Lee

safety

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

Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Bandits

Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning

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