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A distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models.
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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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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J · 2018
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Detecting and correcting for label shift with black box predictors
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Failing loudly: An empirical study of methods for detecting dataset shift
Rabanser, S., Günnemann, S., and Lipton, Z. C · 2018
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Nuanced metrics for measuring unintended bias with real data for text classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., and Vasserman, L · 2019
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Sample complexity of sinkhorn divergences
Genevay, A., Chizat, L., Bach, F., Cuturi, M., and Peyré, G · 2019
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Identifying the instances associated with distribution shifts using the max-sliced bures divergence
Brockmeier, A. J., Claros-Olivares, C. C., Emigh, M., and Giraldo, L. G. S · 2021
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Why did the distribution change?
Budhathoki, K., Janzing, D., Bloebaum, P., and Ng, H · 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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Counterfactual generative networks
Sauer, A. and Geiger, A · 2021
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Shanbhag, A., Ghosh, A., and Rubin, J · 2021
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A survey on optimal transport for machine learning: Theory and applications, 2021
Torres, L. C., Pereira, L. M., and Amini, M. H · 2021
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