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Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model.
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Geodesic flow kernel for unsupervised domain adaptation
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Masashi Sugiyama and Motoaki Kawanabe · 2012
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Regularized learning for domain adaptation under label shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, and Animashree Anandkumar · 2019
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Unlabeled data improves adversarial robustness
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Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
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Fredrik D Johansson, David Sontag, and Rajesh Ranganath · 2019
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Domain adaptation and sample bias correction theory and algorithm for regression
Corinna Cortes and Mehryar Mohri · 2014
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External validity: From do-calculus to transportability across populations
Judea Pearl and Elias Bareinboim · 2014
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Adaptation algorithm and theory based on generalized discrepancy
Corinna Cortes, Mehryar Mohri, and Andrés Muñoz Medina · 2015
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Imagenet large scale visual recognition challenge
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Domain-adversarial training of neural networks
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Scatter component analysis: A unified framework for domain adaptation and domain generalization
Muhammad Ghifary, David Balduzzi, W Bastiaan Kleijn, and Mengjie Zhang · 2016
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On target shift in adversarial domain adaptation
Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, and David Carlson · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Causal dantzig: fast inference in linear structural equation models with hidden variables under additive interventions
Dominik Rothenhäusler, Peter Bühlmann, Nicolai Meinshausen, et al · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Visual-inertial state estimation with pre-integration correction for robust mobile augmented reality
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On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Invariance, causality and robustness
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A unified view of label shift estimation
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Domain adaptation with conditional distribution matching and generalized label shift
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