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Spurious correlations occur when a model learns unreliable features from the data and are a well-known drawback of data-driven learning.
The nature of statistical learning theory
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Bach, F. R. and Jordan, M. I · 2002
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Measuring statistical dependence with hilbert-schmidt norms
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B · 2005
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Challenges of big data analysis
Fan, J., Han, F., and Liu, H · 2014
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Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza h3n2
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Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Deep learning generalizes because the parameter-function map is biased towards simple functions
Valle-Perez, G., Camargo, C. Q., and Louis, A. A · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Adversarial examples are not bugs, they are features
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Shortcut learning in deep neural networks
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2020
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What shapes feature representations? exploring datasets, architectures, and training
Hermann, K. and Lampinen, A · 2020
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Feature noise induces loss discrepancy across groups
Khani, F. and Liang, P · 2020
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Understanding the failure modes of out-of-distribution generalization
Progress and challenges in virus genomic epidemiology
Hill, V., Ruis, C., Bajaj, S., Pybus, O. G., and Kraemer, M. U · 2021
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Removing spurious features can hurt accuracy and affect groups disproportionately
Khani, F. and Liang, P · 2021
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Identifying and mitigating spurious correlations for improving robustness in nlp models
Wang, T., Sridhar, R., Yang, D., and Wang, X · 2021
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Causaladv: Adversarial robustness through the lens of causality
Zhang, Y., Gong, M., Liu, T., Niu, G., Tian, X., Han, B., Schölkopf, B., and Zhang, K · 2021
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Examining and combating spurious features under distribution shift
Zhou, C., Ma, X., Michel, P., and Neubig, G · 2021
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Nagarajan, V., Andreassen, A., and Neyshabur, B · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, S., Raghunathan, A., Koh, P. W., and Liang, P · 2020
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The pitfalls of simplicity bias in neural networks
Shah, H., Tamuly, K., Raghunathan, A., Jain, P., and Netrapalli, P · 2020
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Simple data balancing achieves competitive worst-group-accuracy
Idrissi, B. Y., Arjovsky, M., Pezeshki, M., and Lopez-Paz, D · 2022
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On feature learning in the presence of spurious correlations
Izmailov, P., Kirichenko, P., Gruver, N., and Wilson, A. G · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2022
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