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Machine learning models often perform poorly on subgroups that are underrepresented in the training data.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
Earlier work this paper cites.
Spurious correlation: A causal interpretation
Simon, H. A · 1954
Earlier work this paper cites.
Wordnet: a lexical database for english
Miller, G. A · 1995
Earlier work this paper cites.
An overview of statistical learning theory
Vapnik, V. N · 1999
Earlier work this paper cites.
The class imbalance problem: Significance and strategies
Japkowicz, N · 2000
Earlier work this paper cites.
Dataset shift in machine learning
Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Earlier work this paper cites.
Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G · 2016
Earlier work this paper cites.
Connecting language and vision using crowdsourced dense image annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
Earlier work this paper cites.
Learning deep feature representations with domain guided dropout for person re-identification
Xiao, T., Li, H., Ouyang, W., and Wang, X · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S. R · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Learning models with uniform performance via distributionally robust optimization
Duchi, J. and Namkoong, H · 2018
Earlier work this paper cites.
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
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Fairness without demographics in repeated loss minimization
Hashimoto, T., Srivastava, M., Namkoong, H., and Liang, P · 2018
Earlier work this paper cites.
Domain generalization with adversarial feature learning
Li, H., Pan, S. J., Wang, S., and Kot, A. C · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Earlier work this paper cites.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Earlier work this paper cites.
Scibert: A pretrained language model for scientific text
Beltagy, I., Lo, K., and Cohan, A · 2019
Earlier work this paper cites.
Nuanced metrics for measuring unintended bias with real data for text classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., and Vasserman, L · 2019
Earlier work this paper cites.
Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
Earlier work this paper cites.
Can ai help reduce disparities in general medical and mental health care?
Chen, I. Y., Szolovits, P., and Ghassemi, M · 2019
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al · 2019
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Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs
Johnson, A. E., Pollard, T. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-y., Peng, Y., Lu, Z., Mark, R. G., Berkowitz, S. J., and Horng, S · 2019
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Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
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Targeted supervised contrastive learning for long-tailed recognition
Li, T., Cao, P., Yuan, Y., Fan, L., Yang, Y., Feris, R., Indyk, P., and Katabi, D · 2021
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Just train twice: Improving group robustness without training group information
Liu, E. Z., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P. W., Sagawa, S., Liang, P., and Finn, C · 2021
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Blind pareto fairness and subgroup robustness
Martinez, N. L., Bertran, M. A., Papadaki, A., Rodrigues, M., and Sapiro, G · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
Miller, J. P., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., and Schmidt, L · 2021
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Out-of-distribution generalization in the presence of nuisance-induced spurious correlations
Puli, A. M., Zhang, L. H., Oermann, E. K., and Ranganath, R · 2021
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
Cited alongside, same era.
Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al · 2019
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Carbontracker: Tracking and predicting the carbon footprint of training deep learning models
Anthony, L. F. W., Kanding, B., and Selvan, R · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
Cited alongside, same era.
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Imagenet-21k pretraining for the masses
Ridnik, T., Ben-Baruch, E., Noy, A., and Zelnik-Manor, L · 2021
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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B., Chen, I. Y., and Ghassemi, M · 2021
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How to train your vit? data, augmentation, and regularization in vision transformers
Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L · 2021
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Self-supervised learning disentangled group representation as feature
Wang, T., Yue, Z., Huang, J., Sun, Q., and Zhang, H · 2021
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A fine-grained analysis on distribution shift
Wiles, O., Gowal, S., Stimberg, F., Alvise-Rebuffi, S., Ktena, I., Dvijotham, K., and Cemgil, T · 2021
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Delving into deep imbalanced regression
Yang, Y., Zha, K., Chen, Y.-C., Wang, H., and Katabi, D · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Umix: Improving importance weighting for subpopulation shift via uncertainty-aware mixup
Han, Z., Liang, Z., Yang, F., Liu, L., Li, L., Bian, Y., Zhao, P., Wu, B., Zhang, C., and Yao, J · 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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Are all spurious features in natural language alike? an analysis through a causal lens
Joshi, N., Pan, X., and He, H · 2022
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A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others
Li, Z., Evtimov, I., Gordo, A., Hazirbas, C., Hassner, T., Ferrer, C. C., Xu, C., and Ibrahim, M · 2022
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Liang, W. and Zou, J · 2022
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You only need a good embeddings extractor to fix spurious correlations
Mehta, R., Albiero, V., Chen, L., Evtimov, I., Glaser, T., Li, Z., and Hassner, T · 2022
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
Nam, J., Kim, J., Lee, J., and Shin, J · 2022
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Vision transformers are robust learners
Paul, S. and Chen, P.-Y · 2022
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A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Pfohl, S. R., Zhang, H., Xu, Y., Foryciarz, A., Ghassemi, M., and Shah, N. H · 2022
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Rosenfeld, E., Ravikumar, P., and Risteski, A · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al · 2022
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Revisiting weakly supervised pre-training of visual perception models
Singh, M., Gustafson, L., Adcock, A., de Freitas Reis, V., Gedik, B., Kosaraju, R. P., Mahajan, D., Girshick, R., Dollár, P., and Van Der Maaten, L · 2022
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Invariant feature learning for generalized long-tailed classification
Tang, K., Tao, M., Qi, J., Liu, Z., and Zhang, H · 2022
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On multi-domain long-tailed recognition, imbalanced domain generalization and beyond
Yang, Y., Wang, H., and Katabi, D · 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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Nico++: Towards better benchmarking for domain generalization
Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., and Liu, H · 2022
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Medfair: Benchmarking fairness for medical imaging
Zong, Y., Yang, Y., and Hospedales, T · 2022
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Simper: Simple self-supervised learning of periodic targets
Yang, Y., Liu, X., Wu, J., Borac, S., Katabi, D., Poh, M.-Z., and McDuff, D · 2023
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