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The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off way.
Mathematical methods of organizing and planning production
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Concentration of measure and isoperimetric inequalities in product spaces
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Unsupervised word sense disambiguation rivaling supervised methods
Yarowsky, D · 1995
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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
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Vapnik, V · 1999
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Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., Pereira, F., et al · 2007
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Optimal transport: old and new , volume 338
Villani, C · 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
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Online learning: Random averages, combinatorial parameters, and learnability
Rakhlin, A., Sridharan, K., and Tewari, A · 2010
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. et al · 2013
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Domain-adversarial neural networks
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., and Marchand, M · 2014
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Domain adaptation with regularized optimal transport
Courty, N., Flamary, R., and Tuia, D · 2014
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Continuous manifold based adaptation for evolving visual domains
Hoffman, J., Darrell, T., and Saenko, K · 2014
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Ramp loss linear programming support vector machine
Huang, X., Shi, L., and Suykens, J. A · 2014
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Generalization bounds for time series prediction with non-stationary processes
Kuznetsov, V. and Mohri, M · 2014
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Statistical learning and sequential prediction, 2014
Rakhlin, A. and Sridharan, K · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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A century of portraits: A visual historical record of american high school yearbooks
Ginosar, S., Rakelly, K., Sachs, S., Yin, B., and Efros, A. A · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Learning theory and algorithms for forecasting non-stationary time series
Kuznetsov, V. and Mohri, M · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
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Online learning via sequential complexities
Rakhlin, A., Sridharan, K., and Tewari, A · 2015
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TensorFlow: A system for Large-Scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X · 2016
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Optimal transport for domain adaptation
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A · 2016
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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
Computational optimal transport: With applications to data science
Peyré, G., Cuturi, M., et al · 2019
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Optimal transport for multi-source domain adaptation under target shift
Redko, I., Courty, N., Flamary, R., and Tuia, D · 2019
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On learning invariant representations for domain adaptation
Zhao, H., Des Combes, R. T., Zhang, K., and Gordon, G · 2019
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Confidence regularized self-training
Zou, Y., Yu, Z., Liu, X., Kumar, B. V., and Wang, J · 2019
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Pseudo-labeling and confirmation bias in deep semi-supervised learning
Arazo, E., Ortego, D., Albert, P., O’Connor, N. E., and McGuinness, K · 2020
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Understanding self-training for gradual domain adaptation
Kumar, A., Ma, T., and Liang, P · 2020
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Time series prediction and online learning
Kuznetsov, V. and Mohri, M · 2016
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Statistical learning theory, 2016
Liang, P · 2016
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Deep coral: Correlation alignment for deep domain adaptation, 2016
Sun, B. and Saenko, K · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Joint distribution optimal transportation for domain adaptation
Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
Cited alongside, same era.
Discrepancy-based theory and algorithms for forecasting non-stationary time series
Kuznetsov, V. and Mohri, M · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Liang, J., Hu, D., and Feng, J · 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
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A survey on semi-supervised learning
Van Engelen, J. E. and Hoos, H. H · 2020
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Continuously indexed domain adaptation
Wang, H., He, H., and Katabi, D · 2020
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Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M.-T., Hovy, E., and Le, Q. V · 2020
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Gradual domain adaptation in the wild: When intermediate distributions are absent
Abnar, S., Berg, R. v. d., Ghiasi, G., Dehghani, M., Kalchbrenner, N., and Sedghi, H · 2021
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Gradual domain adaptation without indexed intermediate domains
Chen, H.-Y. and Chao, W.-L · 2021
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2021
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Natural adversarial examples
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D · 2021
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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
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Shifts: A dataset of real distributional shift across multiple large-scale tasks
Malinin, A., Band, N., Gal, Y., Gales, M., Ganshin, A., Chesnokov, G., Noskov, A., Ploskonosov, A., Prokhorenkova, L., Provilkov, I., Raina, V., Raina, V., Roginskiy, D., Shmatova, M., Tigas, P., and Yangel, B · 2021
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Meta pseudo labels
Pham, H., Dai, Z., Xie, Q., and Le, Q. V · 2021
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Extending the WILDS benchmark for unsupervised adaptation
Sagawa, S., Koh, P. W., Lee, T., Gao, I., Xie, S. M., Shen, K., Kumar, A., Hu, W., Yasunaga, M., Marklund, H., Beery, S., David, E., Stavness, I., Guo, W., Leskovec, J., Saenko, K., Hashimoto, T., Levine, S., Finn, C., and Liang, P · 2021
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Pyhealth: A python library for health predictive models
Zhao, Y., Qiao, Z., Xiao, C., Glass, L., and Sun, J · 2021
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Algorithms and theory for supervised gradual domain adaptation
Dong, J., Zhou, S., Wang, B., and Zhao, H · 2022
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Future gradient descent for adapting the temporal shifting data distribution in online recommendation system
Ye, M., Jiang, R., Wang, H., Choudhary, D., Du, X., Bhushanam, B., Mokhtari, A., Kejariwal, A., and qiang liu · 2022
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Online continual adaptation with active self-training
Zhou, S., Zhao, H., Zhang, S., Wang, L., Chang, H., Wang, Z., and Zhu, W · 2022
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