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Missing data is a crucial issue when applying machine learning algorithms to real-world datasets.
Biometrika , 63(3):581–592, 1976
Rubin, D. B · 1976
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Maximum likelihood from incomplete data via the em algorithm
Dempster, A. P., Laird, N. M., and Rubin, D. B · 1977
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Missing value estimation methods for dna microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. B · 2001
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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MissForest a non-parametric missing value imputation for mixed-type data
Stekhoven, D. J. and Buhlmann, P · 2011
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mice: Multivariate imputation by chained equations in r
van Buuren, S. and Groothuis-Oudshoorn, K · 2011
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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What is meant by” missing at random”?
Seaman, S., Galati, J., Jackson, D., and Carlin, J · 2013
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Fast computation of Wasserstein barycenters
Cuturi, M. and Doucet, A · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Learning with a Wasserstein loss
Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M., and Poggio, T · 2015
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Matrix completion and low-rank svd via fast alternating least squares
Hastie, T., Mazumder, R., Lee, J. D., and Zadeh, R · 2015
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Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2016
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missmda: a package for handling missing values in multivariate data analysis
Josse, J., Husson, F., et al · 2016
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Multiple imputation of missing categorical and continuous values via bayesian mixture models with local dependence
Murray, J. S. and Reiter, J. P · 2016
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Learning with minibatch Wasserstein : asymptotic and gradient properties
Fatras, K., Zine, Y., Flamary, R., Gribonval, R., and Courty, N · 2019
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Interpolating between optimal transport and MMD using Sinkhorn divergences
Feydy, J., Séjourné, T., Vialard, F., Amari, S., Trouvé, A., and Peyré, G · 2019
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Variational autoencoder with arbitrary conditioning
Ivanov, O., Figurnov, M., and Vetrov, D · 2019
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Statistical analysis with missing data
Little, R. J. A. and Rubin, D. B · 2019
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MIWAE: Deep generative modelling and imputation of incomplete data sets
Mattei, P.-A. and Frellsen, J · 2019
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R-miss-tastic: a unified platform for missing values methods and workflows
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Learning generative models with Sinkhorn divergences
Genevay, A., Peyre, G., and Cuturi, M · 2018
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Data Missing Not at Random: Jae-Kwang Kim, Zhiliang Ying Editors for this Special Issue
Kim, J. and Ying, Z · 2018
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Improving gans using optimal transport
Salimans, T., Zhang, H., Radford, A., and Metaxas, D · 2018
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Flexible Imputation of Missing Data
van Buuren, S · 2018
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GAIN: Missing data imputation using generative adversarial nets
Yoon, J., Jordon, J., and van der Schaar, M · 2018
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Mayer, I., Josse, J., Tierney, N., and Vialaneix, N · 2019
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Graphical Models for Processing Missing Data
Mohan, K. and Pearl, J · 2019
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Computational optimal transport
Peyré, G., Cuturi, M., et al · 2019
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Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming
Schiebinger, G., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Gould, J., Liu, S., Lin, S., Berube, P., et al · 2019
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Why are big data matrices approximately low rank?
Udell, M. and Townsend, A · 2019
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High-dimensional principal component analysis with heterogeneous missingness
Zhu, Z., Wang, T., and Samworth, R. J · 2019
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