3 papers
cs.LG2024
Preference Learning Algorithms Do Not Learn Preference Rankings
Angelica Chen, Sadhika Malladi, Lily H. Zhang +4
Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner…
cs.LG2024
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
Zhou Lu, Qiuyi Zhang, Xinyi Chen +3
Fast changing states or volatile environments pose a significant challenge to online optimization, which needs to perform rapid adaptation under limited observation. In this paper,…
cs.DS2023
Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products
Tamas Sarlos, Xingyou Song, David Woodruff +2
Inspired by fast algorithms in natural language processing, we study low rank approximation in the entrywise transformed setting where we want to find a good rank approximation…