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Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.
Optimization of conditional value-at-risk
Rockafellar, R. T. and Uryasev, S · 2000
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
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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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The Caltech-UCSD Birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Robust solutions of optimization problems affected by uncertain probabilities
Ben-Tal, A., den Hertog, D., Waegenaere, A. D., Melenberg, B., and Rennen, G · 2013
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Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F · 2013
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Tagging performance correlates with age
Hovy, D. and Søgaard, A · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Quantifying input uncertainty in stochastic optimization
Lam, H. and Zhou, E · 2015
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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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Demographic dialectal variation in social media: A case study of African-American English
Blodgett, S. L., Green, L., and O’Connor, B · 2016
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Statistics of robust optimization: A generalized empirical likelihood approach
Duchi, J., Glynn, P., and Namkoong, H · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebo, N · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Variance regularization with convex objectives
Namkoong, H. and Duchi, J · 2017
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Automatic differentiation in pytorch, 2017
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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On fairness and calibration
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q · 2017
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Gender and dialect bias in YouTube’s automatic captions
Tatman, R · 2017
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Learning non-discriminatory predictors
Woodworth, B., Gunasekar, S., Ohannessian, M. I., and Srebro, N · 2017
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A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., and Wallach, H · 2018
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Multiaccuracy: Black-box post-processing for fairness in classification
Kim, M. P., Ghorbani, A., and Zou, J · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
McCoy, R. T., Pavlick, E., and Linzen, T · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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Distributionally robust language modeling
Oren, Y., Sagawa, S., Hashimoto, T., and Liang, P · 2019
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Meta-Weight-Net: Learning an explicit mapping for sample weighting
Shu, J., Xie, Q., Yi, L., Zhao, Q., Zhou, S., Xu, Z., and Meng, D · 2019
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HuggingFace’s transformers: State-of-the-art natural language processing
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Annotation artifacts in natural language inference data
Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., and Smith, N. A · 2018
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Fairness without demographics in repeated loss minimization
Hashimoto, T. B., Srivastava, M., Namkoong, H., and Liang, P · 2018
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Learning to reweight examples for robust deep learning
Ren, M., Zeng, W., Yang, B., and Urtasun, R · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M. R · 2018
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Deep learning predicts hip fracture using confounding patient and healthcare variables
Badgeley, M. A., Zech, J. R., Oakden-Rayner, L., Glicksberg, B. S., Liu, M., Gale, W., McConnell, M. V., Percha, B., Snyder, T. M., and Dudley, J. T · 2019
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Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., and Brew, J · 2019
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Increasing robustness to spurious correlations using forgettable examples
Yaghoobzadeh, Y., Mehri, S., Tachet, R., Hazen, T. J., and Sordoni, A · 2019
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Heteroskedastic and imbalanced deep learning with adaptive regularization
Cao, K., Chen, Y., Lu, J., Arechiga, N., Gaidon, A., and Ma, T · 2020
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Model patching: Closing the subgroup performance gap with data augmentation
Goel, K., Gu, A., Li, Y., and Ré, C · 2020
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Large-scale methods for distributionally robust optimization
Levy, D., Carmon, Y., Duchi, J. C., and Sidford, A · 2020
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Learning from failure: Training debiased classifier from biased classifier
Nam, J., Cha, H., Ahn, S., Lee, J., and Shin, J · 2020
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Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., and Ré, C · 2020
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Gradient starvation: A learning proclivity in neural networks
Pezeshki, M., Kaba, S.-O., Bengio, Y., Courville, A., Precup, D., and Lajoie, G · 2020
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Sohoni, N. S., Dunnmon, J. A., Angus, G., Gu, A., and Ré, C · 2020
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Towards debiasing NLU models from unknown biases
Utama, P. A., Moosavi, N. S., and Gurevych, I · 2020
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Coping with label shift via distributionally robust optimisation
Zhang, J., Menon, A., Veit, A., Bhojanapalli, S., Kumar, S., and Sra, S · 2020
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Environment inference for invariant learning
Creager, E., Jacobsen, J.-H., and Zemel, R · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., and Liang, P · 2021
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