4 citations · 12 across the 12 of their papers we have counts for
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cs.LG2024★ 1 cited
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik
Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a maj…
cs.GT2024
Axioms for AI Alignment from Human Feedback
Luise Ge, Daniel Halpern, Evi Micha +4
In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on…
cs.LG2024
Preference Poisoning Attacks on Reward Model Learning
Junlin Wu, Jiongxiao Wang, Chaowei Xiao +3
Learning reward models from pairwise comparisons is a fundamental component in a number of domains, including autonomous control, conversational agents, and recommendation systems,…