1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Kernel Density Bayesian Inverse Reinforcement Learning
Aishwarya Mandyam, Didong Li, Jiayu Yao +3
Inverse reinforcement learning (IRL) methods infer an agent's reward function using demonstrations of expert behavior. A Bayesian IRL approach models a distribution over candidate…
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
Aishwarya Mandyam, Andrew Jones, Jiayu Yao +2
Reinforcement learning (RL) is an effective framework for solving sequential decision-making tasks. However, applying RL methods in medical care settings is challenging in part due…