James Jordon

James Jordon

University of Oxford

H-index: 17

Europe-United Kingdom

About James Jordon

James Jordon, With an exceptional h-index of 17 and a recent h-index of 17 (since 2020), a distinguished researcher at University of Oxford, specializes in the field of Machine Learning Theory, Mathematics.

His recent articles reflect a diverse array of research interests and contributions to the field:

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

Synthetic Data--what, why and how?

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

Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis

Generative modelling for supervised, unsupervised and private learning

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

Estimating counterfactual treatment outcomes over time through adversarially balanced representations

James Jordon Information

University

Position

PhD Student

Citations(all)

2929

Citations(since 2020)

2890

Cited By

650

hIndex(all)

17

hIndex(since 2020)

17

i10Index(all)

22

i10Index(since 2020)

22

Email

University Profile Page

Google Scholar

James Jordon Skills & Research Interests

Machine Learning Theory

Mathematics

Top articles of James Jordon

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data

arXiv preprint arXiv:2211.06550

2022/11/12

Synthetic Data--what, why and how?

arXiv preprint arXiv:2205.03257

2022/5/6

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

International Conference on Artificial Intelligence and Statistics (AISTATS)

2023

Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification

2021/8/7

Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis

2021/7/1

Generative modelling for supervised, unsupervised and private learning

2021/1/1

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

arXiv preprint arXiv:2012.04580

2020/12/8

Estimating counterfactual treatment outcomes over time through adversarially balanced representations

IEEE Transactions on Neural Networks and Learning Systems

2024/2/26

Contextual Constrained Learning for Dose-Finding Clinical Trials

2020/6/3

OrganITE: Optimal transplant donor organ offering using an individual treatment effect

Neural Information Processing Systems (NeurIPS)

2020

VIME: Extending the Success of Self-and Semi-supervised Learning to Tabular Domain

Advances in Neural Information Processing Systems

2020

Estimating the effects of continuous-valued interventions using generative adversarial networks

Neural Information Processing Systems (NeurIPS)

2020/2/27

See List of Professors in James Jordon University(University of Oxford)

Co-Authors

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