James Jordon | DeepAI

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Mihaela van der Schaar
138 publications
Adrian Weller
101 publications
Yao Zhang
75 publications
Cheng Zhang
60 publications
Cong Shen
44 publications
Carsten Maple
33 publications
Jinsung Yoon
32 publications
Ahmed M. Alaa
24 publications
Lukasz Szpruch
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Samuel N. Cohen
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Jeroen Berrevoets
15 publications

∙ 11/12/2022

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

Personal data collected at scale promises to improve decision-making and...

0 Florimond Houssiau, et al. ∙

∙ 05/06/2022

Synthetic Data – what, why and how?

This explainer document aims to provide an overview of the current state...

0 James Jordon, et al. ∙

∙ 02/04/2022

To Impute or not to Impute? – Missing Data in Treatment Effect Estimation

Missing data is a systemic problem in practical scenarios that causes no...

5 Jeroen Berrevoets, et al. ∙

∙ 12/08/2020

Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods

Many ground-breaking advancements in machine learning can be attributed ...

13 James Jordon, et al. ∙

∙ 07/23/2020

Hide-and-Seek Privacy Challenge

The clinical time-series setting poses a unique combination of challenge...

11 James Jordon, et al. ∙

∙ 02/27/2020

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

While much attention has been given to the problem of estimating the eff...

0 Ioana Bica, et al. ∙

∙ 02/10/2020

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations

Identifying when to give treatments to patients and how to select among ...

0 Ioana Bica, et al. ∙

∙ 01/08/2020

Contextual Constrained Learning for Dose-Finding Clinical Trials

Clinical trials in the medical domain are constrained by budgets. The nu...

0 Hyun-Suk Lee, et al. ∙

∙ 06/16/2019

ASAC: Active Sensing using Actor-Critic models

Deciding what and when to observe is critical when making observations i...

0 Jinsung Yoon, et al. ∙

∙ 05/29/2019

Lifelong Bayesian Optimization

Automatic Machine Learning (Auto-ML) systems tackle the problem of autom...

0 Yao Zhang, et al. ∙

∙ 06/29/2018

Measuring the quality of Synthetic data for use in competitions

Machine learning has the potential to assist many communities in using t...

0 James Jordon, et al. ∙

∙ 06/07/2018

GAIN: Missing Data Imputation using Generative Adversarial Nets

We propose a novel method for imputing missing data by adapting the well...

0 Jinsung Yoon, et al. ∙

∙ 02/18/2018

RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks

Training complex machine learning models for prediction often requires a...

0 Jinsung Yoon, et al. ∙

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