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While the success of semi-supervised learning (SSL) is still not fully understood, Sch\"olkopf et al.
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“Nonlinear causal discovery with additive noise models”
P.. Hoyer, D. Janzing, J.. Mooij, J. Peters and B. Sch“”olkopf · 2009
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“On the identifiability of the post-nonlinear causal model”
“Graphical models for inference with missing data”
Karthika Mohan, Judea Pearl and Jin Tian · 2013
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“Projected estimators for robust semi-supervised classification”
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Kun Zhang and Aapo Hyv“”arinen · 2009
Cited alongside, same era.
“Semi-Supervised Learning”
Olivier Chapelle, Bernhard Sch“”olkopf and Alexander Zien · 2010
Cited alongside, same era.
“Inferring deterministic causal relations”
Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang and Bernhard Sch“”olkopf · 2010
Cited alongside, same era.
“Causal inference using the algorithmic Markov condition”
Dominik Janzing and Bernhard Sch“”olkopf · 2010
Cited alongside, same era.
“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“Access to unlabeled data can speed up prediction time”
Ruth Urner, Shai Shalev-Shwartz and Shai Ben-David · 2011
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“Information-geometric approach to inferring causal directions”
Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel and Bernhard Sch“”olkopf · 2012
Cited alongside, same era.
Jesse Krijthe and Marco Loog · 2017
Later among the works it cites.
“Elements of Causal Inference - Foundations and Learning Algorithms”
Jonas Peters, Dominik Janzing and Bernhard Sch“”olkopf · 2017
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“Detecting non-causal artifacts in multivariate linear regression models”
Dominik Janzing and Bernhard Sch“”olkopf · 2018
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“The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning”
Jesse Krijthe and Marco Loog · 2018
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“Domain adaptation by using causal inference to predict invariant conditional distributions”
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg and Joris Mooij · 2018
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“Learning Independent Causal Mechanisms”
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla and Bernhard Sch“”olkopf · 2018
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“Invariant models for causal transfer learning”
Mateo Rojas-Carulla, Bernhard Sch“”olkopf, Richard Turner and Jonas Peters · 2018
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“Causal Discovery from Heterogeneous/Nonstationary Data”
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour and Bernhard Sch“”olkopf · 2019
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“Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results”
Alexander Mey and Marco Loog · 2019
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“Semi-generative modelling: covariate-shift adaptation with cause and effect features”
Julius von K“”ugelgen, Alexander Mey and Marco Loog · 2019
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