44 citations · 95 across the 36 of their papers we have counts for
22 papers · 1 filter
Local and Global Stability in Performative Reinforcement Learning
Debmalya Mandal
In performative reinforcement learning the deployed policy shapes the environment that generates the learner's future data, and the natural solution concept is a performatively sta…
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
Peyman Baghershahi, Fangxin Wang, Debmalya Mandal +1
Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) re…
Corruption-robust Offline Multi-agent Reinforcement Learning From Human Feedback
Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban +2
We consider robustness against data corruption in offline multi-agent reinforcement learning from human feedback (MARLHF) under a strong-contamination model: given a dataset of…
On Corruption-Robustness in Performative Reinforcement Learning
Vasilis Pollatos, Debmalya Mandal, Goran Radanovic
In performative Reinforcement Learning (RL), an agent faces a policy-dependent environment: the reward and transition functions depend on the agent's policy. Prior work on performa…
Independent Learning in Performative Markov Potential Games
Rilind Sahitaj, Paulius Sasnauskas, Yiğit Yalın +2
Performative Reinforcement Learning (PRL) refers to a scenario in which the deployed policy changes the reward and transition dynamics of the underlying environment. In this work,…
Policy Teaching via Data Poisoning in Learning from Human Preferences
Andi Nika, Jonathan Nöther, Debmalya Mandal +3
We study data poisoning attacks in learning from human preferences. More specifically, we consider the problem of teaching/enforcing a target policy by synthesizing pre…
Strategyproof Reinforcement Learning from Human Feedback
Thomas Kleine Buening, Jiarui Gan, Debmalya Mandal +1
We study Reinforcement Learning from Human Feedback (RLHF) in settings where multiple labelers may strategically misreport feedback to steer the learned policy toward their own pre…
Distributionally Robust Reinforcement Learning with Human Feedback
Debmalya Mandal, Paulius Sasnauskas, Goran Radanovic
Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs). However, existing RLHF methods are non-rob…
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.…
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,…
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…
Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks
Mohammad Mohammadi, Jonathan Nöther, Debmalya Mandal +2
In targeted poisoning attacks, an attacker manipulates an agent-environment interaction to force the agent into adopting a policy of interest, called target policy. Prior work has…
Online Reinforcement Learning with Uncertain Episode Lengths
Debmalya Mandal, Goran Radanovic, Jiarui Gan +2
Existing episodic reinforcement algorithms assume that the length of an episode is fixed across time and known a priori. In this paper, we consider a general framework of episodic…
Socially Fair Reinforcement Learning
Debmalya Mandal, Jiarui Gan
We consider the problem of episodic reinforcement learning where there are multiple stakeholders with different reward functions. Our goal is to output a policy that is socially fa…
Performative Reinforcement Learning
Debmalya Mandal, Stelios Triantafyllou, Goran Radanovic
We introduce the framework of performative reinforcement learning where the policy chosen by the learner affects the underlying reward and transition dynamics of the environment. F…
Learning Tensor Representations for Meta-Learning
Samuel Deng, Yilin Guo, Daniel Hsu +1
We introduce a tensor-based model of shared representation for meta-learning from a diverse set of tasks. Prior works on learning linear representations for meta-learning assume th…
Feature-based Individual Fairness in k-Clustering
Debajyoti Kar, Mert Kosan, Debmalya Mandal +4
Ensuring fairness in machine learning algorithms is a challenging and essential task. We consider the problem of clustering a set of points while satisfying fairness constraints. W…
Meta-Learning with Graph Neural Networks: Methods and Applications
Debmalya Mandal, Sourav Medya, Brian Uzzi +1
Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. H…
Ensuring Fairness Beyond the Training Data
Debmalya Mandal, Samuel Deng, Suman Jana +2
We initiate the study of fair classifiers that are robust to perturbations in the training distribution. Despite recent progress, the literature on fairness has largely ignored the…
Weighted Tensor Completion for Time-Series Causal Inference
Debmalya Mandal, David Parkes
Marginal Structural Models (MSM) are the most popular models for causal inference from time-series observational data. However, they have two main drawbacks: (a) they do not captur…
Calibrated Fairness in Bandits
Yang Liu, Goran Radanovic, Christos Dimitrakakis +2
We study fairness within the stochastic, \emph{multi-armed bandit} (MAB) decision making framework. We adapt the fairness framework of "treating similar individuals similarly" to t…