1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026★ 1 cited
AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin
Shuo Yang, Qihui Zhang, Yuyang Liu +7
Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We…
cs.LG2026
Learning-To-Measure: In-Context Active Feature Acquisition
Yuta Kobayashi, Zilin Jing, Jiayu Yao +2
Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to…
cs.LG2026
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
Aishwarya Mandyam, Shengpu Tang, Jiayu Yao +2
Off-policy evaluation (OPE) is critical for applying contextual bandit algorithms to high-stakes decision-making settings such as healthcare, where new treatment policies must be e…