17 citations · 59 across the 26 of their papers we have counts for
8 papers · 1 filter
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…
Concentrated Differential Privacy for Bandits
Achraf Azize, Debabrota Basu
Bandits serve as the theoretical foundation of sequential learning and an algorithmic foundation of modern recommender systems. However, recommender systems often rely on user-sens…
Pure Exploration in Bandits with Linear Constraints
Emil Carlsson, Debabrota Basu, Fredrik D. Johansson +1
We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike…
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…
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
Edwige Cyffers, Aurélien Bellet, Debabrota Basu
We study differentially private (DP) machine learning algorithms as instances of noisy fixed-point iterations, in order to derive privacy and utility results from this well-studied…