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A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.
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Geodesic flow kernel for unsupervised domain adaptation
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Categorical reparameterization with Gumbel-softmax
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Optimization as a model for few-shot learning
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Adversarial discriminative domain adaptation
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From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge
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Moment matching for multi-source domain adaptation
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J. Requeima, J. Gordon, J. Bronskill, S. Nowozin, and R. Turner · 2019
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Improving CNN classifiers by estimating test-time priors
M. Sulc and J. Matas · 2019
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RxRx1: An image set for cellular morphological variation across many experimental batches
J. Taylor, B. Earnshaw, B. Mabey, M. Victors, and J. Yosinski · 2019
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Meta-amortized variational inference and learning
M. Wu, K. Choi, N. Goodman, and S. Ermon · 2019
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The iWildCam 2020 competition dataset
S. Beery, E. Cole, and A. Gjoka · 2020
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Empirical Bayes transductive meta-learning with synthetic gradients
S. Hu, P. Moreno, Y. Xiao, X. Shen, G. Obozinski, N. Lawrence, and A. Damianou · 2020
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Be like water: Robustness to extraneous variables via adaptive feature normalization
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Evaluating prediction-time batch normalization for robustness under covariate shift
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
S. Sagawa, P. Koh, T. Hashimoto, and P. Liang · 2020
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Improving robustness against common corruptions by covariate shift adaptation
S. Schneider, E. Rusak, L. Eck, O. Bringmann, W. Brendel, and M. Bethge · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Y. Sun, X. Wang, Z. Liu, J. Miller, A. Efros, and M. Hardt · 2020
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A survey of unsupervised deep domain adaptation
G. Wilson and D. Cook · 2020
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Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
C. Yeh, A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke · 2020
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Tailoring: Encoding inductive biases by optimizing unsupervised objectives at prediction time
F. Alet, M. Bauza, K. Kawaguchi, N. Kuru, T. Lozano-Pérez, and L. Kaelbling · 2021
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Domain generalization by marginal transfer learning
G. Blanchard, A. Deshmukh, U. Dogan, G. Lee, and C. Scott · 2021
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In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
P. Koh, S. Sagawa, H. Marklund, S. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. Phillips, I. Gao, T. Lee, E. David, I. Stavness, W. Guo, B. Earnshaw, I. Haque, S. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang · 2021
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Tent: Fully test-time adaptation by entropy minimization
D. Wang, E. Shelhamer, S. Liu, B. Olshausen, and T. Darrell · 2021
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