17 citations · 59 across the 26 of their papers we have counts for
5 papers · 2 filters
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…
Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback
Riccardo Della Vecchia, Debabrota Basu
Endogeneity, i.e. the dependence of noise and covariates, is a common phenomenon in real data due to omitted variables, strategic behaviours, measurement errors etc. In contrast, t…
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data
Pratik Karmakar, Debabrota Basu
We study design of black-box model extraction attacks that can send minimal number of queries from a publicly available dataset to a target ML model through a predictive API with a…