35 citations · 89 across the 43 of their papers we have counts for
45 papers
The 2026 Singapore Consensus on Global AI Safety Research Priorities
Stephen Casper, Oskar Galeev, Yoshua Bengio +117
Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential to embracing AI with confidence. The 202…
Offline Safe Policy Optimization From Heterogeneous Feedback
Ze Gong, Pradeep Varakantham, Akshat Kumar
Offline Preference-based Reinforcement Learning (PbRL) learns rewards and policies aligned with human preferences without the need for extensive reward engineering and direct inter…
FairVizARD: A Visualization System for Assessing Multi-Party Fairness of Ride-Sharing Matching Algorithms
Ashwin Kumar, Sanket Shah, Meghna Lowalekar +3
There is growing interest in algorithms that match passengers with drivers in ride-sharing problems and their fairness for the different parties involved (passengers, drivers, and…
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…