35 citations · 89 across the 43 of their papers we have counts for
9 papers · 1 filter
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health
Gauri Jain, Pradeep Varakantham, Haifeng Xu +3
Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited…
Offline Safe Reinforcement Learning Using Trajectory Classification
Ze Gong, Akshat Kumar, Pradeep Varakantham
Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most ex…
Preserving the Privacy of Reward Functions in MDPs through Deception
Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
Preserving the privacy of preferences (or rewards) of a sequential decision-making agent when decisions are observable is crucial in many physical and cybersecurity domains. For in…
EduQate: Generating Adaptive Curricula through RMABs in Education Settings
Sidney Tio, Dexun Li, Pradeep Varakantham
There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in de…
Safety through feedback in Constrained RL
Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe beh…
Bootstrapping Language Models with DPO Implicit Rewards
Changyu Chen, Zichen Liu, Chao Du +5
Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the proces…