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Environment annotations are essential for the success of many out-of-distribution (OOD) generalization methods.
Nuanced metrics for measuring unintended bias with real data for text classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., and Vasserman, L · 1903
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 1907
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 1911
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Statistical classification methods in consumer credit scoring: a review
Hand, D. J. and Henley, W. E · 1997
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Statistical learning theory
Vapnik, V · 1998
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The class imbalance problem: Significance and strategies
Japkowicz, N · 2000
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Unshuffling data for improved generalization in visual question answering
Teney, D., Abbasnejad, E., and van den Hengel, A · 2002
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Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., and Courville, A · 2003
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2004
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Making things happen: A theory of causal explanation
Woodward, J · 2005
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Fairness without demographics through adversarially reweighted learning
Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., and Chi, E. H · 2006
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The pitfalls of simplicity bias in neural networks
Shah, H., Tamuly, K., Raghunathan, A., Jain, P., and Netrapalli, P · 2006
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Noise or signal: The role of image backgrounds in object recognition
Xiao, K., Engstrom, L., Ilyas, A., and Madry, A · 2006
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2007
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Learning from failure: Training debiased classifier from biased classifier
Nam, J., Cha, H., Ahn, S., Lee, J., and Shin, J · 2007
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Adaptive risk minimization: Learning to adapt to domain shift
Zhang, M., Marklund, H., Dhawan, N., Gupta, A., Levine, S., and Finn, C · 2007
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What neural networks memorize and why: Discovering the long tail via influence estimation
Feldman, V. and Zhang, C · 2008
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Cifar-10, 2009
Krizhevsky, A., Nair, V., and Hinton, G · 2009
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Causality
Pearl, J · 2009
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Environment inference for invariant learning
Creager, E., Jacobsen, J.-H., and Zemel, R · 2010
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Generalizing from several related classification tasks to a new unlabeled sample
Blanchard, G., Lee, G., and Scott, C · 2011
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Sohoni, N., Dunnmon, J., Angus, G., Gu, A., and Ré, C · 2011
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The Caltech-UCSD birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Ko et al · 2012
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al · 2016
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Just mix once: Worst-group generalization by group interpolation
Giannone, G., Havrylov, S., Massiah, J., Yilmaz, E., and Jiao, Y · 2022
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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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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., and Wang, Y · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Cited alongside, same era.
Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S. R · 2017
Cited alongside, same era.
Liang, W. and Zou, J · 2022
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Measuring and signing fairness as performance under multiple stakeholder distributions
Lopez-Paz, D., Bouchacourt, D., Sagun, L., and Usunier, N · 2022
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Diverse weight averaging for out-of-distribution generalization
Rame, A., Kirchmeyer, M., Rahier, T., Rakotomamonjy, A., Gallinari, P., and Cord, M · 2022
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Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
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Simplicity bias leads to amplified performance disparities
Bell, S. J. and Sagun, L · 2023
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Rethinking invariant graph representation learning without environment partitions
Chen, Y., Bian, Y., Zhou, K., Xie, B., Han, B., and Cheng, J · 2023
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Spuriosity didn’t kill the classifier: Using invariant predictions to harness spurious features
Eastwood, C., Singh, S., Nicolicioiu, A. L., Vlastelica, M., von Kügelgen, J., and Schölkopf, B · 2023
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Crosssplit: mitigating label noise memorization through data splitting
Kim, J., Baratin, A., Zhang, Y., and Lacoste-Julien, S · 2023
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Towards last-layer retraining for group robustness with fewer annotations
LaBonte, T., Muthukumar, V., and Kumar, A · 2023
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Diversify and disambiguate: Learning from underspecified data
Lee, Y., Yao, H., and Finn, C · 2023
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Bias amplification enhances minority group performance
Li, G., Liu, J., and Hu, W · 2023
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Spurious feature diversification improves out-of-distribution generalization
Lin, Y., Tan, L., Hao, Y., Wong, H., Dong, H., Zhang, W., Yang, Y., and Zhang, T · 2023
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Invariant feature regularization for fair face recognition
Ma, J., Yue, Z., Tomoyuki, K., Tomoki, S., Jayashree, K., Pranata, S., and Zhang, H · 2023
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Agree to disagree: Diversity through disagreement for better transferability
Pagliardini, M., Jaggi, M., Fleuret, F., and Karimireddy, S. P · 2023
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Simple and fast group robustness by automatic feature reweighting
Qiu, S., Potapczynski, A., Izmailov, P., and Wilson, A. G · 2023
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Provably invariant learning without domain information
Tan, X., Yong, L., Zhu, S., Qu, C., Qiu, X., Yinghui, X., Cui, P., and Qi, Y · 2023
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Group robust classification without any group information
Tsirigotis, C., Monteiro, J., Rodriguez, P., Vazquez, D., and Courville, A · 2023
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Change is hard: A closer look at subpopulation shift
Yang, Y., Zhang, H., Katabi, D., and Ghassemi, M · 2023
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Semi-supervised domain generalization with known and unknown classes
Zhang, L., Li, J.-F., and Wang, W · 2023
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Does invariant graph learning via environment augmentation learn invariance?
Chen, Y., Bian, Y., Zhou, K., Xie, B., Han, B., and Cheng, J · 2024
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