arXiv Machine Learning August 13, 2026 By Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni

An Efficient Near-Optimal Algorithm for Adversarial $m$-Set Bandits

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arXiv:2608. 12231v1 Announce Type: new Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.

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arXiv Machine Learning

Aug 26

An Efficient Minimax-Optimal Algorithm for Adversarial $m$-Set Bandits

arXiv:2608. 12231v2 Announce Type: replace Abstract: We study adversarial combinatorial bandits with $m$-set actions, where at each round the learner selects $m$ out of $d$ items and observes only the aggregate loss of the selected items.

By Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni

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arXiv Machine Learning

Sep 15

Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary

arXiv:2609.13547v1 Announce Type: new Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...

By Mengxiao Zhang

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Hugging Face Trending Papers

Jul 15

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a context, selects a combinatorial action consisting of a subset of basic arms, and receives the reward of each selected arm; the goal is to maximize the cumulative reward over time.

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 16

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

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

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 1

A Complete Characterization of Learnability for Adversarial Noisy Bandits

arXiv:2605. 09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$.

By Steve Hanneke, Kun Wang

reinforcement-learning safety

arXiv Machine Learning

Jun 2

An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction

arXiv:2508. 11931v3 Announce Type: replace Abstract: We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requiring a linear optimization oracle for the action sets in each round.

By Tim van Erven, Jack Mayo, Julia Olkhovskaya, Chen-Yu Wei

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