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Changes in the data distribution at test time can have deleterious effects on the performance of predictive models $p(y|x)$.
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Connectionist probability estimators in HMM speech recognition
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Improving predictive inference under covariate shift by weighting the log-likelihood function
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Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
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Long-tail learning via logit adjustment
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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The Caltech-UCSD birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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WILDS: A benchmark of in-the-wild distribution shifts
P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, S. Beery, J. Leskovec, A. Kundaje, E. Pierson, S. Levine, C. Finn, and P. Liang · 2012
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On causal and anticausal learning
B. Schoelkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij · 2012
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Detecting and correcting for label shift with black box predictors
Z. Lipton, Y.-X. Wang, and A. Smola · 2018
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A Broad-Coverage challenge corpus for sentence understanding through inference
A. Williams, N. Nangia, and S. Bowman · 2018
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M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
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Fairness and Machine Learning: Limitations and Opportunities
S. Barocas, M. Hardt, and A. Narayanan · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
D. Borkan, L. Dixon, J. Sorensen, N. Thain, and L. Vasserman · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, R. Ball, K. Shpanskaya, J. Seekins, D. A. Mong, S. S. Halabi, J. K. Sandberg, R. Jones, D. B. Larson, C. P. Langlotz, B. N. Patel, M. P. Lungren, and A. Y. Ng · 2019
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
A. Alexandari, A. Kundaje, and A. Shrikumar · 2020
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A unified view of label shift estimation
S. Garg, Y. Wu, S. Balakrishnan, and Z. Lipton · 2020
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Shortcut learning in deep neural networks
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann · 2020
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Deep learning applied to chest x-rays: Exploiting and preventing shortcuts
S. Jabbour, D. Fouhey, E. Kazerooni, M. W. Sjoding, and J. Wiens · 2020
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On feature learning in the presence of spurious correlations
P. Izmailov, P. Kirichenko, N. Gruver, and A. G. Wilson · 2022
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Invariant and transportable representations for Anti-Causal domain shifts
Y. Jiang and V. Veitch · 2022
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Modeling the data-generating process is necessary for out-of-distribution generalization
J. N. Kaur, E. Kiciman, and A. Sharma · 2022
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Towards group robustness in the presence of partial group labels
V. S. Lokhande, K. Sohn, J. Yoon, M. Udell, C.-Y. Lee, and T. Pfister · 2022
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Fairness and robustness in anti-causal prediction
M. Makar and A. D’Amour · 2022
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M. J. Bours · 2021
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In search of lost domain generalization
I. Gulrajani and D. Lopez-Paz · 2021
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Just train twice: Improving group robustness without training group information
E. Z. Liu, B. Haghgoo, A. S. Chen, A. Raghunathan, P. W. Koh, S. Sagawa, P. Liang, and C. Finn · 2021
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BARACK: Partially supervised group robustness with guarantees
N. S. Sohoni, M. Sanjabi, N. Ballas, A. Grover, S. Nie, H. Firooz, and C. Ré · 2021
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Counterfactual invariance to spurious correlations in text classification
V. Veitch, A. D’Amour, S. Yadlowsky, and J. Eisenstein · 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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Memo: Test time robustness via adaptation and augmentation
M. Zhang, S. Levine, and C. Finn · 2021
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Causally motivated shortcut removal using auxiliary labels
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Generative multitask learning mitigates target-causing confounding
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Probabilistic Machine Learning: An introduction
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
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Revisiting self-distillation, 2022
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Out-of-distribution generalization in the presence of Nuisance-Induced spurious correlations
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Simplified transfer learning for chest radiography models using less data
A. B. Sellergren, C. Chen, Z. Nabulsi, Y. Li, A. Maschinot, A. Sarna, J. Huang, C. Lau, S. R. Kalidindi, M. Etemadi, F. Garcia-Vicente, D. Melnick, Y. Liu, K. Eswaran, D. Tse, N. Beladia, D. Krishnan, and S. Shetty · 2022
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A fine-grained analysis on distribution shift
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Causally motivated multi-shortcut identification and removal
J. Zheng and M. Makar · 2022
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A comprehensive survey on test-time adaptation under distribution shifts
J. Liang, R. He, and T. Tan · 2023
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