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In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing characters of a new font using a set of different fonts.
Improving predictive inference under covariate shift by weighting the log-likelihood function
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Rademacher and gaussian complexities: Risk bounds and structural results
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Empirical margin distributions and bounding the generalization error of combined classifiers
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Introduction to statistical learning theory
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Cycada: Cycle-consistent adversarial domain adaptation
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Analysis of representations for domain adaptation
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Instance weighting for domain adaptation in NLP
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Learning bounds for domain adaptation
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Efficient projections onto the l 1-ball for learning in high dimensions
Duchi, J., Shalev-Shwartz, S., Singer, Y., and Chandra, T. (2008) · 2008
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Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Schölkopf, B. (2009) · 2009
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Domain adaptation: Learning bounds and algorithms
Mansour, Y., Mohri, M., and Rostamizadeh, A. (2009a) · 2009
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A survey on transfer learning
Pan, S. J. and Yang, Q. (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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Domain adaptation in regression
Cortes, C. and Mohri, M. (2011) · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011) · 2011
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A two-stage weighting framework for multi-source domain adaptation
Sun, Q., Chattopadhyay, R., Panchanathan, S., and Ye, J. (2011) · 2011
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Marginalized denoising autoencoders for domain adaptation
Chen, M., Xu, Z., Weinberger, K. Q., and Sha, F. (2012) · 2012
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Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K. (2015) · 2015
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Multi-source domain adaptation: A causal view
Zhang, K., Gong, M., and Schölkopf, B. (2015) · 2015
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Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., and Erhan, D. (2016) · 2016
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Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I. (2016) · 2016
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Adversarial discriminative domain adaptation
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Extracting relationships by multi-domain matching
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A kernel two-sample test
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Machine learning in non-stationary environments: Introduction to covariate shift adaptation
Sugiyama, M. and Kawanabe, M. (2012) · 2012
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A survey on concept drift adaptation
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Robust learning under uncertain test distributions: Relating covariate shift to model misspecification
Wen, J., Yu, C.-N., and Greiner, R. (2014) · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V. (2015) · 2015
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Multi-adversarial domain adaptation
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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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Adaptation based on generalized discrepancy
Cortes, C., Mohri, M., and Medina, A. M. (2019) · 2019
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Support and invertibility in domain-invariant representations
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Multi-domain adversarial learning
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On learning invariant representations for domain adaptation
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Domain-adversarial training of neural networks
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