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Gain: Missing data imputation using generative adversarial nets

, , and . International conference on machine learning, page 5689--5698. PMLR, (2018)

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Measuring the quality of Synthetic data for use in competitions., , and . CoRR, (2018)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)Lifelong Bayesian Optimization., , , and . CoRR, (2019)INVASE: Instance-wise Variable Selection using Neural Networks., , and . ICLR (Poster), OpenReview.net, (2019)KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks., , and . ICLR, OpenReview.net, (2019)PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees., , and . ICLR (Poster), OpenReview.net, (2019)GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets., , and . ICLR (Poster), OpenReview.net, (2018)Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods., , and . CoRR, (2020)Hide-and-Seek Privacy Challenge., , , , , , , , , and . CoRR, (2020)