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Transfer learning is essential when sufficient data comes from the source domain, with scarce labeled data from the target domain.
Sample selection bias as a specification error
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Correcting sample selection bias by unlabeled data
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Sinno Jialin Pan and Qiang Yang · 2009
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Learning bounds for importance weighting
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Impossibility theorems for domain adaptation
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Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang · 2010
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Iain M Johnstone · 2011
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Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Do cifar-10 classifiers generalize to cifar-10?
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Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, and Ioannis Mitliagkas · 2019
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Machine learning: a probabilistic perspective
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Unsupervised domain adaptation by domain invariant projection
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Adaptation based on generalized discrepancy
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On the value of target data in transfer learning
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
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Domain adaptation with asymmetrically-relaxed distribution alignment
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On learning invariant representation for domain adaptation
Han Zhao, Remi Tachet des Combes, Kun Zhang, and Geoffrey J Gordon · 2019
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Empirical or invariant risk minimization? a sample complexity perspective
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R Varshney · 2020
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Few-shot learning via learning the representation, provably
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Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks
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Robustness to spurious correlations via human annotations
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Domain adaptation with conditional distribution matching and generalized label shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, and Geoffrey J Gordon · 2020
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Gradual domain adaptation in the wild: When intermediate distributions are absent
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