2 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence
Achraf Azize, Marc Jourdan, Aymen Al Marjani +1
Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tuning hyper-parameters, and conducting u…
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Hannes Eriksson, Debabrota Basu, Tommy Tram +2
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it a…
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits
Achraf Azize, Debabrota Basu
We study the problem of multi-armed bandits with -global Differential Privacy (DP). First, we prove the minimax and problem-dependent regret lower bounds for stochastic and line…