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The problem of covariate-shift generalization has attracted intensive research attention.
Arjovsky, M.; Bottou, L.; Gulrajani, I.; and Lopez-Paz, D. 2019 · 1907
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Sample selection bias as a specification error
Heckman, J. J. 1979 · 1979
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Kohavi, R.; et al. 1996 · 1996
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H. 2000 · 2000
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Analysis of representations for domain adaptation
Ben-David, S.; Blitzer, J.; Crammer, K.; and Pereira, F. 2006 · 2006
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Correcting sample selection bias by unlabeled data
Huang, J.; Gretton, A.; Borgwardt, K.; Schölkopf, B.; and Smola, A. 2006 · 2006
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On model selection consistency of Lasso
Zhao, P.; and Yu, B. 2006 · 2006
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In search of lost domain generalization
Gulrajani, I.; and Lopez-Paz, D. 2020 · 2007
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Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D.; Wakefield, M. J.; Smyth, G. K.; and Oshlack, A. 2009 · 2009
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A theory of learning from different domains
Ben-David, S.; Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F.; and Vaughan, J. W. 2010 · 2010
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Survey sampling
Kish, L. 2011 · 2011
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Unsupervised visual domain adaptation using subspace alignment
Fernando, B.; Habrard, A.; Sebban, M.; and Tuytelaars, T. 2013 · 2013
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Domain generalization via invariant feature representation
Muandet, K.; Balduzzi, D.; and Schölkopf, B. 2013 · 2013
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Domain generalization for object recognition with multi-task autoencoders
Ghifary, M.; Kleijn, W. B.; Zhang, M.; and Balduzzi, D. 2015 · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Learning transferable features with deep adaptation networks
Long, M.; Cao, Y.; Wang, J.; and Jordan, M. 2015 · 2015
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Selective transfer machine for personalized facial expression analysis
Chu, W.-S.; De la Torre, F.; and Cohn, J. F. 2016 · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B.; and Saenko, K. 2016 · 2016
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A survey of transfer learning
Weiss, K.; Khoshgoftaar, T. M.; and Wang, D. 2016 · 2016
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Deeper, broader and artier domain generalization
Li, D.; Yang, Y.; Song, Y.-Z.; and Hospedales, T. M. 2017 · 2017
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Adversarial Discriminative Domain Adaptation
Tzeng, E.; Hoffman, J.; Saenko, K.; and Darrell, T. 2017a · 2017
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Metareg: Towards domain generalization using meta-regularization
Balaji, Y.; Sankaranarayanan, S.; and Chellappa, R. 2018 · 2018
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Stable prediction across unknown environments
Kuang, K.; Cui, P.; Athey, S.; Xiong, R.; and Li, B. 2018 · 2018
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A Survey of Unsupervised Deep Domain Adaptation
Wilson, G.; and Cook, D. J. 2020 · 2020
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Feature selection using stochastic gates
Yamada, Y.; Lindenbaum, O.; Negahban, S.; and Kluger, Y. 2020 · 2020
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FDA: Fourier Domain Adaptation for Semantic Segmentation
Yang, Y.; and Soatto, S. 2020 · 2020
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Domain Generalization by Marginal Transfer Learning
Blanchard, G.; Deshmukh, A. A.; Dogan, Ü.; Lee, G.; and Scott, C. D. 2021 · 2021
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Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)
Gideon, J.; McInnis, M. G.; and Provost, E. M. 2021 · 2021
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Towards non-iid image classification: A dataset and baselines
He, Y.; Shen, Z.; and Cui, P. 2021 · 2021
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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Saito, K.; Watanabe, K.; Ushiku, Y.; and Harada, T. 2018 · 2018
Cited alongside, same era.
How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)
Santurkar, S.; Tsipras, D.; Ilyas, A.; and Madry, A. 2018 · 2018
Cited alongside, same era.
Generalizing across domains via cross-gradient training
Shankar, S.; Piratla, V.; Chakrabarti, S.; Chaudhuri, S.; Jyothi, P.; and Sarawagi, S. 2018 · 2018
Cited alongside, same era.
Causally regularized learning with agnostic data selection bias
Shen, Z.; Cui, P.; Kuang, K.; Li, B.; and Chen, P. 2018 · 2018
Cited alongside, same era.
Domain generalization by solving jigsaw puzzles
Carlucci, F. M.; D’Innocente, A.; Bucci, S.; Caputo, B.; and Tommasi, T. 2019 · 2019
Cited alongside, same era.
Domain generalization via model-agnostic learning of semantic features
Dou, Q.; Coelho de Castro, D.; Kamnitsas, K.; and Glocker, B. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Wilds: A benchmark of in-the-wild distribution shifts
Koh, P. W.; Sagawa, S.; Marklund, H.; Xie, S. M.; Zhang, M.; Balsubramani, A.; Hu, W.; Yasunaga, M.; Phillips, R. L.; Gao, I.; et al. 2021 · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D.; Caballero, E.; Jacobsen, J.-H.; Zhang, A.; Binas, J.; Zhang, D.; Le Priol, R.; and Courville, A. 2021 · 2021
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Towards out-of-distribution generalization: A survey
Shen, Z.; Liu, J.; He, Y.; Zhang, X.; Xu, R.; Yu, H.; and Cui, P. 2021 · 2021
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Overparameterization Improves Robustness to Covariate Shift in High Dimensions
Tripuraneni, N.; Adlam, B.; and Pennington, J. 2021 · 2021
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Generalizing to Unseen Domains: A Survey on Domain Generalization
Wang, J.; Lan, C.; Liu, C.; Ouyang, Y.; and Qin, T. 2021 · 2021
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Deep stable learning for out-of-distribution generalization
Zhang, X.; Cui, P.; Xu, R.; Zhou, L.; He, Y.; and Shen, Z. 2021 · 2021
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Stable learning establishes some common ground between causal inference and machine learning
Cui, P.; and Athey, S. 2022 · 2022
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A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Xu, R.; Zhang, X.; Shen, Z.; Zhang, T.; and Cui, P. 2022 · 2022
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Model Agnostic Sample Reweighting for Out-of-Distribution Learning
Zhou, X.; Lin, Y.; Pi, R.; Zhang, W.; Xu, R.; Cui, P.; and Zhang, T. 2022 · 2022
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Domain-adversarial training of neural networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2030
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