170 citations · 506 across the 51 of their papers we have counts for
55 papers
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A. Suttle, Alec Koppel +4
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy need…
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
Peihong Yu, Manav Mishra, Alec Koppel +5
Multi-Agent Reinforcement Learning (MARL) algorithms face the challenge of efficient exploration due to the exponential increase in the size of the joint state-action space. While…
MaxMin-RLHF: Alignment with Diverse Human Preferences
Souradip Chakraborty, Jiahao Qiu, Hui Yuan +5
Reinforcement Learning from Human Feedback (RLHF) aligns language models to human preferences by employing a singular reward model derived from preference data. However, such an ap…
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +4
We present a novel unified bilevel optimization-based framework, \textsf{PARL}, formulated to address the recently highlighted critical issue of policy alignment in reinforcement l…
A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels
Yifan Yang, Alec Koppel, Zheng Zhang
Learning with noisy labels is an important topic for scalable training in many real-world scenarios. However, few previous research considers this problem in the online setting, wh…
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate
Zhan Gao, Aryan Mokhtari, Alec Koppel
Non-asymptotic convergence analysis of quasi-Newton methods has gained attention with a landmark result establishing an explicit local superlinear rate of O. The…