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One of the central problems in machine learning is domain adaptation.
On learning invariant representation for domain adaptation
Zhao, H., Combes, R. T. d., Zhang, K., and Gordon, G. J. (2019a) · 1901
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Transfer adaptation learning: A decade survey
Zhang, L. (2019) · 1903
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Butterfly: A panacea for all difficulties in wildly unsupervised domain adaptation
Liu, F., Lu, J., Han, B., Niu, G., Zhang, G., and Sugiyama, M. (2019) · 1905
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D. (2019) · 1907
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Improved sample complexities for deep networks and robust classification via an all-layer margin
Wei, C. and Ma, T. (2019) · 1910
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Multi-source domain adaptation for semantic segmentation
Zhao, S., Li, B., Yue, X., Gu, Y., Xu, P., Hu, R., Chai, H., and Keutzer, K. (2019b) · 1910
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P. (2019) · 1911
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Sample selection bias as a specification error
Heckman, J. J. (1979) · 1979
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S. (2018) · 1993
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Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A., and Darrell, T. (2018) · 1998
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H. (2000) · 2000
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Understanding self-training for gradual domain adaptation
Kumar, A., Ma, T., and Liang, P. (2020) · 2002
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Support vector machines for classification in nonstandard situations
Lin, Y., Lee, Y., and Wahba, G. (2002) · 2002
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Priol, R. L., and Courville, A. (2020) · 2003
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Learning and evaluating classifiers under sample selection bias
Zadrozny, B. (2004) · 2004
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Self-training avoids using spurious features under domain shift
Chen, Y., Wei, C., Kumar, A., and Ma, T. (2020c) · 2006
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al. (2020) · 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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Javed, K., White, M., and Bengio, Y. (2020) · 2006
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Rethinking distributional matching based domain adaptation
Li, B., Wang, Y., Che, T., Zhang, S., Zhao, S., Xu, P., Zhou, W., Bengio, Y., and Keutzer, K. (2020) · 2006
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A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A. J. (2007) · 2007
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D. (2020) · 2007
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Direct importance estimation for covariate shift adaptation
Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P., and Kawanabe, M. (2008) · 2008
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Learning explanations that are hard to vary
Parascandolo, G., Neitz, A., Orvieto, A., Gresele, L., and Schölkopf, B. (2020) · 2009
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Dataset shift in machine learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D. (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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Learning bounds for importance weighting
Cortes, C., Mansour, Y., and Mohri, M. (2010) · 2010
Cited alongside, same era.
Representation learning via invariant causal mechanisms
Mitrovic, J., McWilliams, B., Walker, J., Buesing, L., and Blundell, C. (2020) · 2010
Cited alongside, same era.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T. (2010) · 2010
Cited alongside, same era.
Domain adaptation for large-scale sentiment classification: A deep learning approach
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
Cited alongside, same era.
Domain adaptation for object recognition: An unsupervised approach
A dirt-t approach to unsupervised domain adaptation
Shu, R., Bui, H. H., Narui, H., and Ermon, S. (2018) · 2018
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Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
Xu, R., Chen, Z., Zuo, W., Yan, J., and Lin, L. (2018) · 2018
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Adversarial multiple source domain adaptation
Zhao, H., Zhang, S., Wu, G., Moura, J. M., Costeira, J. P., and Gordon, G. J. (2018) · 2018
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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
Berthelot, D., Carlini, N., Cubuk, E. D., Kurakin, A., Sohn, K., Zhang, H., and Raffel, C. (2019) · 2019
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Multi-adversarial faster-rcnn for unrestricted object detection
He, Z. and Zhang, L. (2019) · 2019
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Gopalan, R., Li, R., and Chellappa, R. (2011) · 2011
Cited alongside, same era.
f f -divergence estimation and two-sample homogeneity test under semiparametric density-ratio models
Kanamori, T., Suzuki, T., and Sugiyama, M. (2011) · 2011
Cited alongside, same era.
Robust visual domain adaptation with low-rank reconstruction
Jhuo, I.-H., Liu, D., Lee, D., and Chang, S.-F. (2012) · 2012
Cited alongside, same era.
Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation
Sugiyama, M., Suzuki, T., and Kanamori, T. (2012) · 2012
Cited alongside, same era.
Fundamental limits and tradeoffs in invariant representation learning
Zhao, H., Dan, C., Aragam, B., Jaakkola, T. S., Gordon, G. J., and Ravikumar, P. (2020a) · 2012
Cited alongside, same era.
Non-linear domain adaptation with boosting
Becker, C. J., Christoudias, C. M., and Fua, P. (2013) · 2013
Cited alongside, same era.
Domain adaptation under target and conditional shift
Zhang, K., Schölkopf, B., Muandet, K., and Wang, Z. (2013) · 2013
Cited alongside, same era.
Sliced wasserstein discrepancy for unsupervised domain adaptation
Lee, C.-Y., Batra, T., Baig, M. H., and Ulbricht, D. (2019) · 2019
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Unsupervised domain adaptation using feature-whitening and consensus loss
Roy, S., Siarohin, A., Sangineto, E., Bulo, S. R., Sebe, N., and Ricci, E. (2019) · 2019
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Transferable curriculum for weakly-supervised domain adaptation
Shu, Y., Cao, Z., Long, M., and Wang, J. (2019) · 2019
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Domain adaptation with asymmetrically-relaxed distribution alignment
Wu, Y., Winston, E., Kaushik, D., and Lipton, Z. (2019) · 2019
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Multi-level domain adaptive learning for cross-domain detection
Xie, R., Yu, F., Wang, J., Wang, Y., and Zhang, L. (2019) · 2019
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Bridging theory and algorithm for domain adaptation
Zhang, Y., Liu, T., Long, M., and Jordan, M. (2019) · 2019
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Adapting object detectors via selective cross-domain alignment
Zhu, X., Pang, J., Yang, C., Shi, J., and Lin, D. (2019) · 2019
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Invariant risk minimization games
Ahuja, K., Shanmugam, K., Varshney, K., and Dhurandhar, A. (2020) · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A. (2020) · 2020
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Transfer-learning-library
Junguang Jiang, Bo Fu, M. L. (2020) · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C. A., Cubuk, E. D., Kurakin, A., and Li, C.-L. (2020) · 2020
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Unsupervised data augmentation for consistency training
Xie, Q., Dai, Z., Hovy, E., Luong, T., and Le, Q. (2020) · 2020
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A comprehensive survey on transfer learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q. (2020) · 2020
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{BREEDS}: Benchmarks for subpopulation shift
Santurkar, S., Tsipras, D., and Madry, A. (2021) · 2021
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Theoretical analysis of self-training with deep networks on unlabeled data
Wei, C., Shen, K., Chen, Y., and Ma, T. (2021) · 2021
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How neural networks extrapolate: From feedforward to graph neural networks
Xu, K., Zhang, M., Li, J., Du, S. S., Kawarabayashi, K.-I., and Jegelka, S. (2021) · 2021
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Towards a theoretical framework of out-of-distribution generalization
Ye, H., Xie, C., Cai, T., Li, R., Li, Z., and Wang, L. (2021) · 2021
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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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Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., and Grauman, K. (2012) · 2073
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