44 citations · 95 across the 36 of their papers we have counts for
9 papers · 1 filter
Surprisingly Popular Voting for Concentric Rank-Order Models
Hadi Hosseini, Debmalya Mandal, Amrit Puhan
An important problem on social information sites is the recovery of ground truth from individual reports when the experts are in the minority. The wisdom of the crowd, i.e. the col…
Performative Reinforcement Learning with Linear Markov Decision Process
Debmalya Mandal, Goran Radanovic
We study the setting of \emph{performative reinforcement learning} where the deployed policy affects both the reward, and the transition of the underlying Markov decision process.…
The Surprising Effectiveness of SP Voting with Partial Preferences
Hadi Hosseini, Debmalya Mandal, Amrit Puhan
We consider the problem of recovering the ground truth ordering (ranking, top-, or others) over a large number of alternatives. The wisdom of crowd is a heuristic approach based…
Symmetric Linear Bandits with Hidden Symmetry
Nam Phuong Tran, The Anh Ta, Debmalya Mandal +1
High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure…
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
Andi Nika, Debmalya Mandal, Adish Singla +1
We study data corruption robustness in offline two-player zero-sum Markov games. Given a dataset of realized trajectories of two players, an adversary is allowed to modify an -f…
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban +3
In this paper, we take a step towards a deeper understanding of learning from human preferences by systematically comparing the paradigm of reinforcement learning from human feedba…
Performative Reinforcement Learning in Gradually Shifting Environments
Ben Rank, Stelios Triantafyllou, Debmalya Mandal +1
When Reinforcement Learning (RL) agents are deployed in practice, they might impact their environment and change its dynamics. We propose a new framework to model this phenomenon,…
Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
Nam Phuong Tran, The Anh Ta, Shuqing Shi +3
Reward allocation, also known as the credit assignment problem, has been an important topic in economics, engineering, and machine learning. An important concept in reward allocati…
Corruption Robust Offline Reinforcement Learning with Human Feedback
Debmalya Mandal, Andi Nika, Parameswaran Kamalaruban +2
We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting. Given an offline dataset of pairs of trajectories along with feedba…