6 papers
Robust Peak-cost Constrained Reinforcement Learning
Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar +3
We study robust peak-cost constrained reinforcement learning (\ours), where the objective is to maximize expected reward while controlling the maximum cost encountered along a traj…
Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
Sourav Ganguly, Kartik Pandit, Arnob Ghosh
Real-world decision-making systems operate in environments where state transitions depend not only on the agent's actions, but also on \textbf{exogenous factors outside its control…
Provably Efficient Sample Complexity for Robust CMDP
Sourav Ganguly, Arnob Ghosh
We study the problem of learning policies that maximize cumulative reward while satisfying safety constraints, even when the real environment differs from a simulator or nominal mo…
Certifiable Safe RLHF: Semantic Grounding and Fixed Penalty Constraint Optimization for Safer LLM Alignment
Kartik Pandit, Sourav Ganguly, Arnesh Banerjee +2
Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility of model outputs and mitigating their…
Efficient Policy Optimization in Robust Constrained MDPs with Iteration Complexity Guarantees
Sourav Ganguly, Kishan Panaganti, Arnob Ghosh +1
Constrained decision-making is essential for designing safe policies in real-world control systems, yet simulated environments often fail to capture real-world adversities. We cons…
Restricted Bayesian Neural Network
Sourav Ganguly, Saprativa Bhattacharjee
Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions.…