arXiv Machine Learning June 2, 2026 By Youngmin Oh

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

Read the original on arXiv Machine Learning →

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.

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

safety

arXiv Machine Learning

Jun 16

Near-Optimal Stochastic Linear Bandits with Delay

arXiv:2606. 16656v1 Announce Type: new Abstract: We study stochastic linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees.

By Ofir Schlisselberg, Mengxiao Zhang, Yishay Mansour

benchmarks safety

arXiv AI

Jul 15

Learning When to Trust in Contextual Social Bandits

arXiv:2603. 13356v2 Announce Type: replace Abstract: Robust reinforcement learning typically assumes that feedback sources are either globally trustworthy or corrupted within a fixed global budget.

By Majid Ghasemi, Mark Crowley

reinforcement-learning safety

arXiv Machine Learning

Jun 11

Capacity-Constrained Online Convex Optimization with Delayed Feedback

arXiv:2606. 11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives.

By Alexander Ryabchenko, Idan Attias, Daniel M. Roy

reinforcement-learning

arXiv Machine Learning

Aug 4

A Perturbation Approach to Unconstrained Linear Bandits

arXiv:2603. 28201v3 Announce Type: replace Abstract: We revisit the standard perturbation-based approach of Abernethy et al.

By Andrew Jacobsen, Dorian Baudry, Shinji Ito, Nicol\`o Cesa-Bianchi

reinforcement-learning safety

arXiv Machine Learning

1d ago

Lipschitz Bandits with Arbitrary Feedback Delays

arXiv:2608. 15036v1 Announce Type: new Abstract: The Lipschitz bandit problem extends the traditional multi-armed bandit framework to continuous action spaces by assuming that the reward functions satisfy a Lipschitz condition.

By Yuhao Liu, Yu Chen, Longbo Huang

reinforcement-learning safety

arXiv Machine Learning

Jun 19

Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses

arXiv:2606. 19891v1 Announce Type: new Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth.

By Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto

reinforcement-learning safety

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