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Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications.
A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Learning from labeled and unlabeled data with label propagation
Xiaojin Zhu and Zoubin Ghahramani · 2002
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas Lal, Jason Weston, and Bernhard Schölkopf · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Co-training and expansion: Towards bridging theory and practice
Maria-Florina Balcan, Avrim Blum, and Ke Yang · 2004
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Semi-supervised learning on riemannian manifolds
Mikhail Belkin and Partha Niyogi · 2004
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
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All of nonparametric statistics
Larry Wasserman · 2006
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Label propagation through linear neighborhoods
Fei Wang and Changshui Zhang · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Manifold-based similarity adaptation for label propagation
Masayuki Karasuyama and Hiroshi Mamitsuka · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Scaling graph-based semi supervised learning to large number of labels using count-min sketch
Partha Talukdar and William Cohen · 2014
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Improving distant supervision for information extraction using label propagation through lists
Lidong Bing, Sneha Chaudhari, Richard C Wang, and William Cohen · 2015
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Deep convolutional networks on graph-structured data (2015)
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Camlp: Confidence-aware modulated label propagation
Yuto Yamaguchi, Christos Faloutsos, and Hiroyuki Kitagawa · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Training complex models with multi-task weak supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré · 2019
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Learning from rules generalizing labeled exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi · 2020
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Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Ré · 2020
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Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin Benson · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Laine Samuli and Aila Timo · 2017
Cited alongside, same era.
Learning random-walk label propagation for weakly-supervised semantic segmentation
Paul Vernaza and Manmohan Chandraker · 2017
Cited alongside, same era.
When does label propagation fail? a view from a network generative model
Yuto Yamaguchi and Kohei Hayashi · 2017
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
Cited alongside, same era.
Hongwei Wang and Jure Leskovec · 2020
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
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Constrained labeling for weakly supervised learning
Chidubem Arachie and Bert Huang · 2021
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A theory of label propagation for subpopulation shift
Tianle Cai, Ruiqi Gao, Jason Lee, and Qi Lei · 2021
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On the equivalence of decoupled graph convolution network and label propagation
Hande Dong, Jiawei Chen, Fuli Feng, Xiangnan He, Shuxian Bi, Zhaolin Ding, and Peng Cui · 2021
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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Algorithms with predictions
Michael Mitzenmacher and Sergei Vassilvitskii · 2021
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DP-SSL: Towards robust semi-supervised learning with a few labeled samples
Yi Xu, Jiandong Ding, Lu Zhang, and Shuigeng Zhou · 2021
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WRENCH: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 2021
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Data consistency for weakly supervised learning
Chidubem Arachie and Bert Huang · 2022
Closest in time.
Shoring up the foundations: Fusing model embeddings and weak supervision
Mayee F Chen, Daniel Y Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Ré · 2022
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Learning predictions for algorithms with predictions
Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, and Sergei Vassilvitskii · 2022
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