108 citations · 174 across the 8 of their papers we have counts for
9 papers · 1 filter
Synthetic Data -- what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau +5
This explainer document aims to provide an overview of the current state of the rapidly expanding work on synthetic data technologies, with a particular focus on privacy. The artic…
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
James Jordon, Alan Wilson, Mihaela van der Schaar
Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Unfortunately, many large-scale datasets are highly sens…
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…
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…
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
Ioana Bica, James Jordon, Mihaela van der Schaar
While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting o…
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…