most citedEstimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

31 citations · 50 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20205 cited

Hide-and-Seek Privacy Challenge

James Jordon, Daniel Jarrett, Jinsung Yoon +7

The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identifi…

stat.ML20204 cited

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…

cs.LG202031 cited

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

Ioana Bica, Ahmed M. Alaa, James Jordon +1

Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, w…

stat.ML20196 cited

Lifelong Bayesian Optimization

Yao Zhang, James Jordon, Ahmed M. Alaa +1

Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Baye…

cs.LG20194 cited

ASAC: Active Sensing using Actor-Critic models

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Deciding what and when to observe is critical when making observations is costly. In a medical setting where observations can be made sequentially, making these observations (or no…