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Theory

2022

  • A Characterization of Semi-Supervised Adversarially Robust PAC Learnability. [pdf]
    • Idan Attias, Steve Hanneke, Yishay Mansour. Neurips 2022

2021

  • Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning. [pdf] -Vivien Cabannes, Loucas Pillaud-Vivien, Francis Bach, Alessandro Rudi. NeurIPS 2021

2020

  • Semi-Supervised Learning with Meta-Gradient. [pdf]

    • Xin-Yu Zhang, Hao-Lin Jia, Taihong Xiao, Ming-Ming Cheng, Ming-Hsuan Yang. Preprint 2020

  • TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning. [pdf]

    • Xinwei Sun, Yilun Xu, Peng Cao, Yuqing Kong, Lingjing Hu, Shanghang Zhang, Yizhou Wang. ECCV 2020

  • Meta-Semi: A Meta-learning Approach for Semi-supervised Learning. [pdf]

    • Yulin Wang, Jiayi Guo, Shiji Song, Gao Huang. Preprint 2020

  • Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning. [pdf]

    • Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing. Preprint 2020

2019

  • The information-theoretic value of unlabeled data in semi-supervised learning. [pdf]

    • Alexander Golovnev, David Pal, Balazs Szorenyi. ICML 2019

  • Analysis of Network Lasso for Semi-Supervised Regression. [pdf]

    • Alexander Jung, Natalia Vesselinova. AISTATS 2019

  • Semi-supervised clustering for de-duplication. [pdf]

    • Shrinu Kushagra, Shai Ben-David, Ihab Ilyas. AISTATS 2019

  • Learning to Impute: A General Framework for Semi-supervised Learning. [pdf] [code]

    • Wei-Hong Li, Chuan-Sheng Foo, Hakan Bilen. Preprint 2019

2018

  • Semi-Supervised Learning with Competitive Infection Models. [pdf]

    • Nir Rosenfeld, Amir Globerson. AISTATS 2018

  • The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. [pdf]

    • Jesse H. Krijthe, Marco Loog. NeurIPS 2018

  • The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. [pdf]

    • Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing. NeurIPS 2018

2017

  • Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data. [pdf]
    • Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama. ICML 2017

2016

  • Semi-Supervised Learning with Adaptive Spectral Transform. [pdf]

    • Hanxiao Liu, Yiming Yang. AISTATS 2016

  • Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation. [pdf]

    • Sujith Ravi, Qiming Diao. AISTATS 2016

2014

  • Wasserstein Propagation for Semi-Supervised Learning. [pdf]

    • Justin Solomon, Raif Rustamov, Leonidas Guibas, Adrian Butscher. ICML 2014

  • High Order Regularization for Semi-Supervised Learning of Structured Output Problems. [pdf]

    • Yujia Li, Rich Zemel. ICML 2014

2013

  • Correlated random features for fast semi-supervised learning. [pdf]

    • Brian McWilliams, David Balduzzi, Joachim M. Buhmann. NeurIPS 2013

  • Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning. [pdf]

    • Gang Niu, Wittawat Jitkrittum, Bo Dai, Hirotaka Hachiya, Masashi Sugiyama. ICML 2013

  • Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines. [pdf]

    • Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi, Masashi Sugiyama. ICML 2013

  • Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion. [pdf]

    • Jinfeng Yi, Lijun Zhang, Rong Jin, Qi Qian, Anil Jain. ICML 2013

2012

  • A Simple Algorithm for Semi-supervised Learning withImproved Generalization Error Bound. [pdf]

    • Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han. ICML 2012

  • Deterministic Annealing for Semi-Supervised Structured Output Learning. [pdf]

    • Paramveer Dhillon, Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, Sundararajan Sellamanickam. AISTATS 2012

2011

  • Semi-supervised Learning by Higher Order Regularization. [pdf]

    • Xueyuan Zhou, Mikhail Belkin. AISTATS 2011

  • Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. [pdf]

    • Xueyuan Zhou, Nathan Srebro. AISTATS 2011

2010

  • Semi-Supervised Dimension Reduction for Multi-Label Classification. [pdf]

    • Buyue Qian, Ian Davidson. AAAI 2010

  • Semi-Supervised Learning via Generalized Maximum Entropy. [pdf]

    • Ayse Erkan, Yasemin Altun. AISTATS 2010

  • Semi-supervised learning by disagreement. [pdf]

    • Zhi-Hua Zhou, Ming Li. Knowledge and Information Systems 2010

2009

  • Semi-supervised Learning by Sparse Representation. [pdf]
    • Shuicheng Yan and Huan Wang. SIAM 2009

2008

  • Worst-case analysis of the sample complexity of semi-supervised learning. [pdf]
    • Shai Ben-David, Tyler Lu, David Pal. COLT 2008

2007

  • Generalization error bounds in semi-supervised classification under the cluster assumption. [pdf]
    • Philippe Rigollet. JMLR 2007

2005

  • Semi-supervised learning by entropy minimization. [pdf]

    • Yves Grandvalet, Yoshua Bengio. NeurIPS 2005

  • A co-regularization approach to semi-supervised learning with multiple views. [pdf]

    • Vikas Sindhwani, Partha Niyogi, Mikhail Belkin. ICML 2005

  • Tri-Training: Exploiting Unlabeled DataUsing Three Classifiers. [pdf]

    • Zhou Zhi-Hua and Li Ming. IEEE Transactions on knowledge and Data Engineering 2005

2003

  • Semi-supervised learning using gaussian fields and harmonic functions. [pdf]

    • Xiaojin Zhu, Zoubin Ghahramani, John Lafferty. ICML 2003

  • Semi-supervised learning of mixture models. [pdf]

    • Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar Cirelo. ICML 2003

2002

  • Learning from labeled and unlabeled data with label propagation. [pdf]
    • Xiaojin Zhu, Zoubin Ghahramani. NeurIPS 2002

1998

  • Combining labeled and unlabeled data with co-training. [pdf]
    • Tom Michael Mitchell, Tom Mitchell. COLT 1998