James Jordon

According to our database, James Jordon authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Reinforcement Learning applied to Insurance Portfolio Pursuit.

Edward James Young, Alistair Rogers, Elliott Tong, James Jordon

CoRR, 2024

2023

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

Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data.

Florimond Houssiau, James Jordon, Samuel N. Cohen, Owen Daniel, Andrew Elliott, James Geddes, Callum D. Mole, Camila Rangel Smith, Lukasz Szpruch

CoRR, 2022

Synthetic Data - what, why and how?

James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N. Cohen, Adrian Weller

CoRR, 2022

2021

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

Jeroen Berrevoets, Ahmed M. Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

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

James Jordon, Alan Wilson, Mihaela van der Schaar

CoRR, 2020

Hide-and-Seek Privacy Challenge.

James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar

CoRR, 2020

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

Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

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

Ioana Bica, James Jordon, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Estimating counterfactual treatment outcomes over time through adversarially balanced representations.

Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar

Proceedings of the 8th International Conference on Learning Representations, 2020

Contextual Constrained Learning for Dose-Finding Clinical Trials.

Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Lifelong Bayesian Optimization.

Yao Zhang, James Jordon, Ahmed M. Alaa, Mihaela van der Schaar

CoRR, 2019

Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate.

James Jordon, Jinsung Yoon, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

ASAC: Active Sensing using Actor-Critic models.

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Proceedings of the Machine Learning for Healthcare Conference, 2019

INVASE: Instance-wise Variable Selection using Neural Networks.

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Proceedings of the 7th International Conference on Learning Representations, 2019

PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees.

James Jordon, Jinsung Yoon, Mihaela van der Schaar

Proceedings of the 7th International Conference on Learning Representations, 2019

KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks.

James Jordon, Jinsung Yoon, Mihaela van der Schaar

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Measuring the quality of Synthetic data for use in competitions.

James Jordon, Jinsung Yoon, Mihaela van der Schaar

CoRR, 2018

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

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Proceedings of the 35th International Conference on Machine Learning, 2018

GAIN: Missing Data Imputation using Generative Adversarial Nets.

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Proceedings of the 35th International Conference on Machine Learning, 2018

GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets.

Jinsung Yoon, James Jordon, Mihaela van der Schaar

Proceedings of the 6th International Conference on Learning Representations, 2018

Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks.

Onur Atan, James Jordon, Mihaela van der Schaar

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018