1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Robust Reinforcement Learning from Corrupted Human Feedback
Alexander Bukharin, Ilgee Hong, Haoming Jiang +4
Reinforcement learning from human feedback (RLHF) provides a principled framework for aligning AI systems with human preference data. For various reasons, e.g., personal bias, cont…
cs.LG2024★ 1 cited
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
Ilgee Hong, Zichong Li, Alexander Bukharin +4
Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values by learning rewards from human preference data. Due to various reaso…
math.OC2023
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
Ilgee Hong, Sen Na, Michael W. Mahoney +1
We consider solving equality-constrained nonlinear, nonconvex optimization problems. This class of problems appears widely in a variety of applications in machine learning and engi…