170 citations · 506 across the 51 of their papers we have counts for
8 papers · 1 filter
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…
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
Donghao Ying, Yunkai Zhang, Yuhao Ding +2
We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraint…
Sharpened Lazy Incremental Quasi-Newton Method
Aakash Lahoti, Spandan Senapati, Ketan Rajawat +1
The problem of minimizing the sum of functions in dimensions is ubiquitous in machine learning and statistics. In many applications where the number of observations is…
Scalable Multi-Agent Reinforcement Learning with General Utilities
Donghao Ying, Yuhao Ding, Alec Koppel +1
We study the scalable multi-agent reinforcement learning (MARL) with general utilities, defined as nonlinear functions of the team's long-term state-action occupancy measure. The o…