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Domain Adaptation (DA) enables transferring a learning machine from a labeled source domain to an unlabeled target one.
Verification of forecasts expressed in terms of probability
G. W. BRIER · 1950
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On measures of information and entropy
A. Rényi · 1961
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The comparison and evaluation of forecasters
M. H. DeGroot and S. E. Fienberg · 1983
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Inferences for case-control and semiparametric two-sample density ratio models
J. Qin · 1998
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. C. Platt · 1999
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
B. Zadrozny and C. Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
B. Zadrozny and C. Elkan · 2002
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Semiparametric density estimation under a two-sample density ratio model
K. F. Cheng and C. K. Chu · 2004
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Evaluating predictive uncertainty challenge
J. Q. Candela, C. E. Rasmussen, F. H. Sinz, O. Bousquet, and B. Schölkopf · 2005
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Correcting sample selection bias by unlabeled data
J. Huang, A. J. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf · 2006
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Dirichlet-enhanced spam filtering based on biased samples
S. Bickel and T. Scheffer · 2007
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
J. Blitzer, M. Dredze, and F. Pereira · 2007
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Covariate shift adaptation by importance weighted cross validation
M. Sugiyama, M. Krauledat, and K.-R. MÞller · 2007
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
T. Hastie, R. Tibshirani, and J. H. Friedman · 2009
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Dataset Shift in Machine Learning
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence · 2009
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Learning bounds for importance weighting
C. Cortes, Y. Mansour, and M. Mohri · 2010
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A survey on transfer learning
S. J. Pan and Q. Yang · 2010
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Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
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Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
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Optimal kernel choice for large-scale two-sample tests
A. Gretton, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, K. Fukumizu, and B. K. Sriperumbudur · 2012
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Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation
B. Gong, K. Grauman, and F. Sha · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
Cited alongside, same era.
Deep Hashing Network for Unsupervised Domain Adaptation
H. Venkateswara, J. Eusebio, S. Chakraborty, and S. Panchanathan · 2017
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Detecting and correcting for label shift with black box predictors
Z. C. Lipton, Y. Wang, and A. J. Smola · 2018
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Transferable representation learning with deep adaptation networks
M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan · 2018
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Conditional adversarial domain adaptation
M. Long, Z. Cao, J. Wang, and M. I. Jordan · 2018
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Multi-adversarial domain adaptation
Z. Pei, Z. Cao, M. Long, and J. Wang · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada · 2018
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Cited alongside, same era.
Weight uncertainty in neural network
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Cited alongside, same era.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. I. Jordan · 2015
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
M. P. Naeini, G. F. Cooper, and M. Hauskrecht · 2015
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Cited alongside, same era.
Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Cited alongside, same era.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Y. Wen, P. Vicol, J. Ba, D. Tran, and R. B. Grosse · 2018
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Regularized learning for domain adaptation under label shifts
K. Azizzadenesheli, A. Liu, F. Yang, and A. Anandkumar · 2019
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Verified uncertainty calibration
A. Kumar, P. Liang, and T. Ma · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Y. Ovadia, E. Fertig, and J. Ren · 2019
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Moment matching for multi-source domain adaptation
X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang · 2019
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Learning robust global representations by penalizing local predictive power
H. Wang, S. Ge, E. P. Xing, and Z. C. Lipton · 2019
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Transferable normalization: Towards improving transferability of deep neural networks
X. Wang, Y. Jin, M. Long, J. Wang, and M. I. Jordan · 2019
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Transferable attention for domain adaptation
X. Wang, L. Li, W. Ye, M. Long, and J. Wang · 2019
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Unsupervised domain adaptation: An adaptive feature norm approach
R. Xu, G. Li, J. Yang, and L. Lin · 2019
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Towards accurate model selection in deep unsupervised domain adaptation
K. You, X. Wang, M. Long, and M. Jordan · 2019
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Bridging theory and algorithm for domain adaptation
Y. Zhang, T. Liu, M. Long, and M. Jordan · 2019
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On learning invariant representation for domain adaptation
H. Zhao, R. T. des Combes, K. Zhang, and G. J. Gordon · 2019
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
M. A. Alexandari, A. Kundaje, and A. Shrikumar · 2020
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Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations
S. Cui, S. Wang, J. Zhuo, L. Li, Q. Huang, and Q. Tian · 2020
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Calibrated prediction with covariate shift via unsupervised domain adaptation
S. Park, O. Bastani, J. Weimer, and I. Lee · 2020
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