599 citations · 859 across the 13 of their papers we have counts for
13 papers
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
Florimond Houssiau, James Jordon, Samuel N. Cohen +6
Personal data collected at scale promises to improve decision-making and accelerate innovation. However, sharing and using such data raises serious privacy concerns. A promising so…
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…
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono +2
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missin…
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…
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…
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…
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…
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…
Measuring the quality of Synthetic data for use in competitions
James Jordon, Jinsung Yoon, Mihaela van der Schaar
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available. Unfortunately, much of that potential is not be…
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon, James Jordon, Mihaela van der Schaar
We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial I…
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Jinsung Yoon, James Jordon, Mihaela van der Schaar
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related…