17 citations · 59 across the 26 of their papers we have counts for
6 papers · 1 filter
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning
Reda Ouhamma, Debabrota Basu, Odalric-Ambrym Maillard
We study the problem of episodic reinforcement learning in continuous state-action spaces with unknown rewards and transitions. Specifically, we consider the setting where the rewa…
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…
How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis
Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel
Fairness in machine learning has attained significant focus due to the widespread application in high-stake decision-making tasks. Unregulated machine learning classifiers can exhi…
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics
Yannis Flet-Berliac, Debabrota Basu
Although Reinforcement Learning (RL) is effective for sequential decision-making problems under uncertainty, it still fails to thrive in real-world systems where risk or safety is…
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
Hannes Eriksson, Debabrota Basu, Mina Alibeigi +1
In stochastic games with incomplete information, the uncertainty is evoked by the lack of knowledge about a player's own and the other players' types, i.e. the utility function and…
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm
Debabrota Basu, Odalric-Ambrym Maillard, Timothée Mathieu
We study the corrupted bandit problem, i.e. a stochastic multi-armed bandit problem with unknown reward distributions, which are heavy-tailed and corrupted by a history-indepen…