33 citations · 51 across the 14 of their papers we have counts for
3 papers · 1 filter
Reliable Scale-Free Bandits via Empirical Likelihood
Pratham Gupta, Alhad Sethi, Shubhada Agrawal
We study stochastic bandits without knowledge of reward scales or tail parameters. Almost-sure regret guarantees describe eventual learning but do not quantify expected performance…
Best Arm Identification for Bandits with Shifting Means
Lukas Zierahn, Wouter M. Koolen, Shubhada Agrawal +2
We study the best arm identification problem in a stochastic environment with a novel form of adversarial perturbations, which we coin Shifting Means. While classically the mean re…
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Shubhada Agrawal, Timothée Mathieu, Debabrota Basu +1
We investigate the regret-minimisation problem in a multi-armed bandit setting with arbitrary corruptions. Similar to the classical setup, the agent receives rewards generated inde…