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In unsupervised domain adaptation, existing theory focuses on situations where the source and target domains are close.
Combining labeled and unlabeled data with co-training
A. Blum and T. Mitchell · 1998
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Improving predictive inference under covariate shift by weighting the log-likelihood function
H. Shimodaira · 2000
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Semi-supervised learning with explicit misclassification modeling
M. Amini and P. Gallinari · 2003
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Entropy regularization
Y. Grandvalet and Y. Bengio · 2005
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Correcting sample selection bias by unlabeled data
H. Jiayuan, S. A. J., G. Arthur, B. K. M., and S. Bernhard · 2006
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Cubic regularization of newton method and its global performance
Yurii Nesterov and Boris T Polyak · 2006
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Generalization error bounds in semi-supervised classification under the cluster assumption
P. Rigollet · 2007
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Covariate shift adaptation by importance weighted cross validation
M. Sugiyama, M. Krauledat, and K. Muller · 2007
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Does unlabeled data provably help? worst-case analysis of the sample complexity of semi-supervised learning
S. Ben-David, T. Lu, and D. Pal · 2008
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Unlabeled data: Now it helps, now it doesn’t
A. Singh, R. Nowak, and J. Zhu · 2008
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A discriminative model for semi-supervised learning
Maria-Florina Balcan and Avrim Blum · 2010
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Overcoming dataset bias: An unsupervised domain adaptation approach
Boqing Gong, Fei Sha, and Kristen Grauman · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
D. Lee · 2013
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Transfer feature learning with joint distribution adaptation
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu · 2013
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
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Escaping from saddle points—online stochastic gradient for tensor decomposition
Rong Ge, Furong Huang, Chi Jin, and Yang Yuan · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Causal inference using invariant prediction: identification and confidence intervals, 2015
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2015
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A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2015
Invariant risk minimization, 2019
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Unlabeled data improves adversarial robustness
Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi · 2019
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Adversarial examples are not bugs, they are features
A. Ilyas, S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry · 2019
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Learning not to learn: Training deep neural networks with biased data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Are labels required for improving adversarial robustness?
J. Uesato, J. Alayrac, P. Huang, R. Stanforth, A. Fawzi, and P. Kohli · 2019
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Finding approximate local minima faster than gradient descent
Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, and Tengyu Ma · 2017
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Conditional variance penalties and domain shift robustness
C. Heinze-Deml and N. Meinshausen · 2017
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Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
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Adversarially robust generalization requires more data
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Learning robust representations by projecting superficial statistics out
Haohan Wang, Zexue He, Zachary C. Lipton, and Eric P. Xing · 2019
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Bridging theory and algorithm for domain adaptation
Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael I Jordan · 2019
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Confidence regularized self-training
Y. Zou, Z. Yu, X. Liu, B. Kumar, and J. Wang · 2019
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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 2020
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Understanding self-training for gradual domain adaptation
A. Kumar, T. Ma, and P. Liang · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
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Self-training with noisy student improves imagenet classification
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