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cs.LG2026
Beyond Reward Suppression: Near-Optimal Offline Attacks on Warm-Start Bandits with Bounded Rewards
Qirun Zeng, Manhin Poon, Xiangxiang Dai +2
Adversarial attacks on bandits aim to mislead a learner toward a target arm while keeping the attack cost small. Existing attacks typically achieve this by suppressing non-target a…
cs.LG2025
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
Qirun Zeng, Eric He, Richard Hoffmann +2
Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting the…
cs.LG2025
Fusing Reward and Dueling Feedback in Stochastic Bandits
Xuchuang Wang, Qirun Zeng, Jinhang Zuo +4
This paper investigates the fusion of absolute (reward) and relative (dueling) feedback in stochastic bandits, where both feedback types are gathered in each decision round. We der…