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Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve.
Inference and missing data
Rubin, D. B · 1976
Earlier work this paper cites.
Learning sparse nonparametric DAGs
Zheng, X., Dan, C., Aragam, B., Ravikumar, P., and Xing, E. P · 1976
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Dempster, A. P., Laird, N. M., and Rubin, D. B · 1977
Earlier work this paper cites.
An algorithm for fast recovery of sparse causal graphs
Spirtes, P. and Glymour, C · 1991
Earlier work this paper cites.
Causation, prediction, and search
Spirtes, P., Glymour, C. N., Scheines, R., and Heckerman, D · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
Chickering, D. M · 2002
Earlier work this paper cites.
Dealing with missing data
Scheffer, J · 2002
Earlier work this paper cites.
Finding optimal gene networks using biological constraints
Ott, S. and Miyano, S · 2003
Earlier work this paper cites.
Large-sample learning of bayesian networks is np-hard
Chickering, M., Heckerman, D., and Meek, C · 2004
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., and Nolan, G. P · 2005
Earlier work this paper cites.
Finding optimal Bayesian networks by dynamic programming
Singh, A. P. and Moore, A. W · 2005
Earlier work this paper cites.
A bayesian approach to causal discovery
Heckerman, D., Meek, C., and Cooper, G · 2006
Earlier work this paper cites.
mice: Multivariate imputation by chained equations in r
Buuren, S. v. and Groothuis-Oudshoorn, K · 2010
Earlier work this paper cites.
A bayesian approach to constraint based causal inference
Claassen, T. and Heskes, T · 2012
Earlier work this paper cites.
Missforest—non-parametric missing value imputation for mixed-type data
Stekhoven, D. J. and Bühlmann, P · 2012
Earlier work this paper cites.
Ordering-based search: A simple and effective algorithm for learning bayesian networks
Teyssier, M. and Koller, D · 2012
Earlier work this paper cites.
Learning sparse causal gaussian networks with experimental intervention: regularization and coordinate descent
Fu, F. and Zhou, Q · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. and Welling, M · 2013
Earlier work this paper cites.
Integrated systems approach identifies genetic nodes and networks in late-onset alzheimer’s disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al · 2013
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Time-varying learning and content analytics via sparse factor analysis
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Concave penalized estimation of sparse gaussian bayesian networks
Aragam, B. and Zhou, Q · 2015
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Learning bayesian networks with thousands of variables
Scanagatta, M., de Campos, C. P., Corani, G., and Zaffalon, M · 2015
Cited alongside, same era.
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Cited alongside, same era.
Learning treewidth-bounded bayesian networks with thousands of variables
Review of causal discovery methods based on graphical models
Glymour, C., Zhang, K., and Spirtes, P · 2019
Later among the works it cites.
Icebreaker: Element-wise active information acquisition with bayesian deep latent gaussian model
Gong, W., Tschiatschek, S., Turner, R., Nowozin, S., Hernández-Lobato, J. M., and Zhang, C · 2019
Later among the works it cites.
Penalized estimation of directed acyclic graphs from discrete data
Gu, J., Fu, F., and Zhou, Q · 2019
Later among the works it cites.
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Kingma, D. P., Welling, M., et al · 2019
Later among the works it cites.
Gradient-based neural dag learning
Lachapelle, S., Brouillard, P., Deleu, T., and Lacoste-Julien, S · 2019
Later among the works it cites.
EDDI: Efficient dynamic discovery of high-value information with partial VAE
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Scanagatta, M., Corani, G., De Campos, C. P., and Zaffalon, M · 2016
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Zero shot learning for code education: Rubric sampling with deep learning inference
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Implicit graph neural networks
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Castle: Regularization via auxiliary causal graph discovery
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Handling incomplete heterogeneous data using vaes
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Educational question mining at scale: Prediction, analysis and personalization
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Slaps: Self-supervision improves structure learning for graph neural networks
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Results and insights from diagnostic questions: The neurips 2020 education challenge
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