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Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 1905
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Probability of error of some adaptive pattern-recognition machines
Henry Scudder · 1965
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Training a 3-node neural network is np-complete
Avrim L Blum and Ronald L Rivest · 1992
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2005
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Semi-supervised self-training of object detection models
Chuck Rosenberg, Martial Hebert, and Henry Schneiderman · 2005
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Generalization error bounds in semi-supervised classification under the cluster assumption
Philippe Rigollet · 2007
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Agnostically learning halfspaces
Adam Tauman Kalai, Adam R Klivans, Yishay Mansour, and Rocco A Servedio · 2008
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Unlabeled data: Now it helps, now it doesn’t
Aarti Singh, Robert Nowak, and Jerry Zhu · 2008
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A discriminative model for semi-supervised learning
Maria-Florina Balcan and Avrim Blum · 2010
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Multi-view object class detection with a 3d geometric model
Joerg Liebelt and Cordelia Schmid · 2010
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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The impact of unlabeled patterns in rademacher complexity theory for kernel classifiers
Luca Oneto, Davide Anguita, Alessandro Ghio, and Sandro Ridella · 2011
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Shifting weights: Adapting object detectors from image to video
Kevin Tang, Vignesh Ramanathan, Fei-Fei Li, and Daphne Koller · 2012
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User-friendly tail bounds for sums of random matrices
Joel A Tropp · 2012
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Matrix analysis , volume 169
Rajendra Bhatia · 2013
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Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
Boqing Gong, Kristen Grauman, and Fei Sha · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Virtual and real world adaptation for pedestrian detection
David Vazquez, Antonio M Lopez, Javier Marin, Daniel Ponsa, and David Geronimo · 2013
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Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Tensor factorization via matrix factorization
Volodymyr Kuleshov, Arun Chaganty, and Percy Liang · 2015
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Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Training deep neural networks on noisy labels with bootstrapping
Scott E Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan · 2016
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi, Adel Javanmard, and Jason D Lee · 2018
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang · 2018
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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Learning two layer rectified neural networks in polynomial time
Ainesh Bakshi, Rajesh Jayaram, and David P Woodruff · 2019
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 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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L1-regularized neural networks are improperly learnable in polynomial time
Yuchen Zhang, Jason D. Lee, and Michael I. Jordan · 2016
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Globally optimal gradient descent for a convnet with gaussian inputs
Alon Brutzkus and Amir Globerson · 2017
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Convergence analysis of two-layer neural networks with ReLU activation
Yuanzhi Li and Yang Yuan · 2017
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Deep self-learning from noisy labels
Jiangfan Han, Ping Luo, and Xiaogang Wang · 2019
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Revisiting self-training for neural sequence generation
Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato · 2019
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Overcoming catastrophic forgetting with unlabeled data in the wild
Kibok Lee, Kimin Lee, Jinwoo Shin, and Honglak Lee · 2019
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Learning relu networks on linearly separable data: Algorithm, optimality, and generalization
Gang Wang, Georgios B Giannakis, and Jie Chen · 2019
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Billion-scale semi-supervised learning for image classification
I Zeki Yalniz, Hervé Jégou, Kan Chen, Manohar Paluri, and Dhruv Mahajan · 2019
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Learning one-hidden-layer relu networks via gradient descent
Xiao Zhang, Yaodong Yu, Lingxiao Wang, and Quanquan Gu · 2019
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Guaranteed recovery of one-hidden-layer neural networks via cross entropy
Haoyu Fu, Yuejie Chi, and Yingbin Liang · 2020
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Self-training for end-to-end speech recognition
Jacob Kahn, Ann Lee, and Awni Hannun · 2020
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Statistical and algorithmic insights for semi-supervised learning with self-training
Samet Oymak and Talha Cihad Gulcu · 2020
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Understanding and mitigating the tradeoff between robustness and accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang · 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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When does self-supervision improve few-shot learning?
Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan · 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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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Guaranteed convergence of training convolutional neural networks via accelerated gradient descent
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong · 2020
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Fast learning of graph neural networks with guaranteed generalizability:one-hidden-layer case
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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Why lottery ticket wins? a theoretical perspective of sample complexity on pruned neural networks
Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, and Jinjun Xiong · 2021
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