8 citations · 8 across the 10 of their papers we have counts for
Showing 2025 · cs.LGShow all
2 papers · 2 filters
cs.LG2025
A General Framework for Off-Policy Learning with Partially-Observed Reward
Rikiya Takehi, Masahiro Asami, Kosuke Kawakami +1
Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under p…
cs.LG2025
Prompt Optimization with Logged Bandit Data
Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito +1
We study how to use naturally available user feedback, such as clicks, to optimize large language model (LLM) pipelines for generating personalized sentences using prompts. Naive a…