Stebėti
James Jordon
James Jordon
Research Assistant, The Alan Turing Institute
Patvirtintas el. paštas turing.ac.uk
Pavadinimas
Cituota
Cituota
Metai
Gain: Missing data imputation using generative adversarial nets
J Yoon, J Jordon, M Schaar
International Conference on Machine Learning, 5689-5698, 2018
14782018
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
J Jordon, J Yoon, M van der Schaar
8482018
GANITE: Estimation of individualized treatment effects using generative adversarial nets
J Yoon, J Jordon, M Van Der Schaar
International Conference on Learning Representations, 2018
5202018
VIME: Extending the Success of Self-and Semi-supervised Learning to Tabular Domain
J Yoon, Y Zhang, J Jordon, M van der Schaar
Advances in Neural Information Processing Systems 33, 2020
2932020
Synthetic Data--what, why and how?
J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ...
arXiv preprint arXiv:2205.03257, 2022
2362022
INVASE: Instance-wise Variable Selection using Neural Networks
J Yoon, J Jordon, M van der Schaar
2082018
Estimating counterfactual treatment outcomes over time through adversarially balanced representations
I Bica, AM Alaa, J Jordon, M van der Schaar
arXiv preprint arXiv:2002.04083, 2020
2032020
Lifelong Bayesian Optimization
Y Zhang, J Jordon, AM Alaa, M van der Schaar
arXiv preprint arXiv:1905.12280, 2019
133*2019
Estimating the effects of continuous-valued interventions using generative adversarial networks
I Bica, J Jordon, M van der Schaar
Advances in Neural Information Processing Systems 33, 16434-16445, 2020
1252020
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
J Jordon, J Yoon, M van der Schaar
952018
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks.
O Atan, J Jordon, M van der Schaar
AAAI, 2018
862018
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
J Yoon, J Jordon, M van der Schaar
arXiv preprint arXiv:1802.06403, 2018
582018
Measuring the quality of Synthetic data for use in competitions
J Jordon, J Yoon, M van der Schaar
arXiv preprint arXiv:1806.11345, 2018
502018
Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification
J Jordon, D Jarrett, E Saveliev, J Yoon, P Elbers, P Thoral, A Ercole, ...
NeurIPS 2020 Competition and Demonstration Track, 206-215, 2021
482021
OrganITE: Optimal transplant donor organ offering using an individual treatment effect
J Berrevoets, J Jordon, I Bica, M van der Schaar
Advances in Neural Information Processing Systems 33, 2020
482020
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
F Houssiau, J Jordon, SN Cohen, O Daniel, A Elliott, J Geddes, C Mole, ...
arXiv preprint arXiv:2211.06550, 2022
462022
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
J Jordon, J Yoon, M van der Schaar
Advances in Neural Information Processing Systems, 4325-4334, 2019
282019
To Impute or not to Impute?--Missing Data in Treatment Effect Estimation
J Berrevoets, F Imrie, T Kyono, J Jordon, M van der Schaar
arXiv preprint arXiv:2202.02096, 2022
262022
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
J Jordon, A Wilson, M van der Schaar
arXiv preprint arXiv:2012.04580, 2020
252020
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
J Berrevoets, A Alaa, Z Qian, J Jordon, AES Gimson, M Van Der Schaar
International Conference on Machine Learning, 792-802, 2021
202021
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Straipsniai 1–20