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

, , , , , , , , , and . NeurIPS (Competition and Demos), volume 133 of Proceedings of Machine Learning Research, page 206-215. PMLR, (2020)

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Gain: Missing data imputation using generative adversarial nets, , and . International conference on machine learning, page 5689--5698. PMLR, (2018)ASAC: Active Sensing using Actor-Critic models., , and . MLHC, volume 106 of Proceedings of Machine Learning Research, page 451-473. PMLR, (2019)Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods., , and . CoRR, (2020)Lifelong Bayesian Optimization., , , and . CoRR, (2019)GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets., , and . ICLR (Poster), OpenReview.net, (2018)INVASE: Instance-wise Variable Selection using Neural Networks., , and . ICLR (Poster), OpenReview.net, (2019)PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees., , and . ICLR (Poster), OpenReview.net, (2019)KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks., , and . ICLR, OpenReview.net, (2019)Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks., , and . AAAI, page 2071-2078. AAAI Press, (2018)Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis., , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 792-802. PMLR, (2021)