Hugging Face Trending Papers July 16, 2026

MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits

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We design and analyze \underline{M}echanism-\underline{E}nforced \underline{S}equential \underline{HA}lving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic linear bandits. In this setting, each arm may strategically misreport its feature vector to maximize the probability of being identified as the best arm, when rewards are generated from the arms' true but unobservable features.

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benchmarks

arXiv Machine Learning

Jul 17

MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits

arXiv:2607. 14706v1 Announce Type: new Abstract: We design and analyze \underline{M}echanism-\underline{E}nforced \underline{S}equential \underline{HA}lving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic linear bandits.

By Xin Li, Zixin Zhong

benchmarks

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 2

Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

arXiv:2605. 01752v4 Announce Type: replace Abstract: We study linear dueling bandits in volatile environments characterized by the simultaneous presence of post-serving contexts, delayed feedback, and adversarial corruption.

By Youngmin Oh

safety

arXiv Machine Learning

Jun 16

Best Arm Identification with Minimal Regret

arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.

By Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin

agents reinforcement-learning

arXiv Machine Learning

Jun 9

Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees

arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.

By Deqi Zheng, Xiaoyang Xu, Yuhong Yang

reinforcement-learning benchmarks

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

One email a morning, machine-written

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