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Empirical risk minimization (ERM) is sensitive to spurious correlations in the training data, which poses a significant risk when deploying systems trained under this paradigm in high-stake applications.
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On feature learning in the presence of spurious correlations
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Investigating why contrastive learning benefits robustness against label noise
Y. Xue, K. Whitecross, and B. Mirzasoleiman · 2022
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Privacyalert: A dataset for image privacy prediction
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The hidden uniform cluster prior in self-supervised learning
M. Assran, R. Balestriero, Q. Duval, F. Bordes, I. Misra, P. Bojanowski, P. Vincent, M. Rabbat, and N. Ballas · 2023
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A cookbook of self-supervised learning
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Last layer re-training is sufficient for robustness to spurious correlations
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Avoiding spurious correlations via logit correction
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How robust is unsupervised representation learning to distribution shift?
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Distributionally robust post-hoc classifiers under prior shifts
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