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cs.LG2024
Learning Linear Utility Functions From Pairwise Comparison Queries
Luise Ge, Brendan Juba, Yevgeniy Vorobeychik
We study learnability of linear utility functions from pairwise comparison queries. In particular, we consider two learning objectives. The first objective is to predict out-of-sam…
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…