4 papers
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…
Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback
Qirun Zeng, Xuchuang Wang, Jiayi Shen +3
We study fixed-confidence best arm identification in generalized linear bandits under a hybrid feedback model: at each round, the learner may query either (i) absolute reward feedb…
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…
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…