44 citations · 95 across the 36 of their papers we have counts for
6 papers · 1 filter
Inference-Time Nash Alignment
Hadi Hosseini, Debmalya Mandal, Duohan Zhang
Preference-based fine-tuning methods such as RLHF and DPO require substantial compute and large preference datasets. They also need direct access to the model parameters which are…
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…
Meta-Learning Preferences for Multilingual LLM Alignment
Jiaying Lin, Seongho Son, Nam Phuong Tran +3
Unequal availability of human preference data across languages poses a significant challenge for aligning large language models in multilingual settings. To address the lack of suf…
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…
SP-Rank: A Dataset for Ranked Preferences with Secondary Information
Hadi Hosseini, Debmalya Mandal, Amrit Puhan
We introduce , the first large-scale, publicly available dataset for benchmarking algorithms that leverage both first-order preferences and second-order predictio…