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Semi-supervised learning is a setting in which one has labeled and unlabeled data available.
Maximum likelihood from incomplete data via the em algorithm
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A probability analysis on the value of unlabeled data for classification problems
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Stability and generalization
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Learning the kernel matrix with semidefinite programming
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Generalization error bounds using unlabeled data
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B. Leskes · 2005
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
M. Belkin, P. Niyogi, and V. Sindhwani · 2006
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Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers , chapter 4, pages 57–72
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Stable transductive learning
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On bayesian transduction: Implications for the covariate shift problem
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A discriminative model for semi-supervised learning
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Semi-Supervised Learning
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Towards making unlabeled data never hurt
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Active property testing
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Foundations of Machine Learning
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On causal and anticausal learning
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Graph Kernels by Spectral Transforms , chapter 15, pages 277–291
X. Zhu, J. Kandola, J. Lafferty, and Z. Ghahramani · 2006
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Local polynomial regression on unknown manifolds , volume Volume 54 of Lecture Notes–Monograph Series , pages 177–186
P. J. Bickel and B. Li · 2007
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On the effectiveness of laplacian normalization for graph semi-supervised learning
R. Johnson and T. Zhang · 2007
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Statistical analysis of semi-supervised regression
J. D. Lafferty and L. A. Wasserman · 2007
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Generalization error bounds in semi-supervised classification under the cluster assumption
P. Rigollet · 2007
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The rademacher complexity of co-regularized kernel classes
D. S. Rosenberg and P. L. Bartlett · 2007
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B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij · 2012
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Density-sensitive semisupervised inference
M. Azizyan, A. Singh, L. Wasserman, et al · 2013
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Unlabeled data does provably help
M. Darnstädt, H. U. Simon, and B. Szörényi · 2013
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Semi-supervised learning with density-ratio estimation
M. Kawakita and T. Kanamori · 2013
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Manifold regularization and semi-supervised learning: some theoretical analyses
P. Niyogi · 2013
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Understanding Machine Learning: From Theory to Algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Contrastive pessimistic likelihood estimation for semi-supervised classification
M. Loog · 2016
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Rademacher complexity bounds for a penalized multiclass semi-supervised algorithm
Y. Maximov, M.-R. Amini, and Z. Harchaoui · 2016
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Effective semisupervised learning on manifolds
A. Globerson, R. Livni, and S. Shalev-Shwartz · 2017
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Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
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The pessimistic limits of margin-based losses in semi-supervised learning
J. Krijthe and M. Loog · 2018
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The information-theoretic value of unlabeled data in semi-supervised learning
A. Golovnev, D. Pal, and B. Szorenyi · 2019
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When can unlabeled data improve the learning rate?
C. Göpfert, S. Ben-David, O. Bousquet, S. Gelly, I. Tolstikhin, and R. Urner · 2019
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A distribution dependent and independent complexity analysis of manifold regularization, 2019
A. Mey, T. Viering, and M. Loog · 2019
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Semi-generative modelling: Covariate-shift adaptation with cause and effect features
J. von Kügelgen, A. Mey, and M. Loog · 2019
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