| Gain: Missing data imputation using generative adversarial nets J Yoon, J Jordon, M Schaar International Conference on Machine Learning, 5689-5698, 2018 | 904 | 2018 |
| PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees J Jordon, J Yoon, M van der Schaar | 396 | 2018 |
| GANITE: Estimation of individualized treatment effects using generative adversarial nets J Yoon, J Jordon, M Van Der Schaar International Conference on Learning Representations, 2018 | 334 | 2018 |
| INVASE: Instance-wise Variable Selection using Neural Networks J Yoon, J Jordon, M van der Schaar | 150 | 2018 |
| 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 | 130 | 2020 |
| 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 | 117 | 2020 |
| Lifelong Bayesian Optimization Y Zhang, J Jordon, AM Alaa, M van der Schaar arXiv preprint arXiv:1905.12280, 2019 | 79* | 2019 |
| KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks J Jordon, J Yoon, M van der Schaar | 70 | 2018 |
| Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks. O Atan, J Jordon, M van der Schaar AAAI, 2018 | 63 | 2018 |
| 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 | 60 | 2020 |
| 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 | 44 | 2018 |
| Synthetic Data--what, why and how? J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ... arXiv preprint arXiv:2205.03257, 2022 | 43 | 2022 |
| Measuring the quality of Synthetic data for use in competitions J Jordon, J Yoon, M van der Schaar arXiv preprint arXiv:1806.11345, 2018 | 38 | 2018 |
| 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 | 31 | 2020 |
| 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 | 27 | 2021 |
| 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 | 19 | 2019 |
| 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 | 15 | 2020 |
| 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 | 12 | 2021 |
| Contextual Constrained Learning for Dose-Finding Clinical Trials HS Lee, C Shen, J Jordon, M van der Schaar arXiv preprint arXiv:2001.02463, 2020 | 12 | 2020 |
| 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 | 8 | 2022 |
Mihaela van der SchaarUniversity of Cambridge, The Alan Turing InstituteVerified email at ee.ucla.edu
