1 citations · 1 across the 5 of their papers we have counts for
5 papers
LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning
Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh +1
We introduce a unified framework that seamlessly integrates algorithmic recourse, contextual bandits, and large language models (LLMs) to support sequential decision-making in high…
Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine
Prateek Jaiswal, Esmaeil Keyvanshokooh, Junyu Cao
Randomized clinical trials often require large patient cohorts before drawing definitive conclusions, yet abundant observational data from parallel studies remains underutilized du…
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit
Junyu Cao, Ruijiang Gao, Esmaeil Keyvanshokooh
Human doctors frequently recommend actionable recourses that allow patients to modify their conditions to access more effective treatments. Inspired by such healthcare scenarios, w…
Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health
Xueqing Liu, Kyra Gan, Esmaeil Keyvanshokooh +1
Motivated by applications in digital health, this work studies the novel problem of online uniform sampling (OUS), where the goal is to distribute a sampling budget uniformly acros…
Contextual Bandits with Budgeted Information Reveal
Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu +1
Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often re…