8 citations · 20 across the 5 of their papers we have counts for
6 papers
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Hyun-Suk Lee, Cong Shen, William Zame +2
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD). This task becomes significantly more challenging when mul…
Design and Analysis of Uplink and Downlink Communications for Federated Learning
Sihui Zheng, Cong Shen, Xiang Chen
Communication has been known to be one of the primary bottlenecks of federated learning (FL), and yet existing studies have not addressed the efficient communication design, partic…
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen, Zhiyang Wang, Sofia S. Villar +1
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite t…
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Hyun-Suk Lee, Yao Zhang, William Zame +3
Subgroup analysis of treatment effects plays an important role in applications from medicine to public policy to recommender systems. It allows physicians (for example) to identify…
Contextual Constrained Learning for Dose-Finding Clinical Trials
Hyun-Suk Lee, Cong Shen, James Jordon +1
Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, thi…
Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching
Xianzhe Xu, Meixia Tao, Cong Shen
This paper investigates learning-based caching in small-cell networks (SCNs) when user preference is unknown. The goal is to optimize the cache placement in each small base station…