119 citations · 884 across the 130 of their papers we have counts for
33 papers · 1 filter
FIRM: Federated In-client Regularized Multi-objective Alignment for Large Language Models
Fatemeh Nourzad, Amirhossein Roknilamouki, Eylem Ekici +2
Aligning Large Language Models (LLMs) with human values often involves balancing multiple, conflicting objectives such as helpfulness and harmlessness. Training these models is com…
Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping
Yining Li, Peizhong Ju, Ness Shroff
Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of…
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes
Peizhong Ju, Sen Lin, Mark S. Squillante +2
Transfer learning is a useful technique for achieving improved performance and reducing training costs by leveraging the knowledge gained from source tasks and applying it to targe…
Near-Optimal Partially Observable Reinforcement Learning with Partial Online State Information
Ming Shi, Yingbin Liang, Ness B. Shroff
Partially observable Markov decision processes (POMDPs) are a general framework for sequential decision-making under latent state uncertainty, yet learning in POMDPs is intractable…
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Peizhong Ju, Arnob Ghosh, Ness B. Shroff
Fairness plays a crucial role in various multi-agent systems (e.g., communication networks, financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov…
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning
Peizhong Ju, Yingbin Liang, Ness B. Shroff
Meta-learning has arisen as a successful method for improving training performance by training over many similar tasks, especially with deep neural networks (DNNs). However, the th…