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The success of DNNs is driven by the counter-intuitive ability of over-parameterized networks to generalize, even when they perfectly fit the training data.
Generalization and Parameter Estimation in Feedforward Nets: Some Experiments , pp. 630–637
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A simple weight decay can improve generalization
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Training cost-sensitive neural networks with methods addressing the class imbalance problem
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Dropout: A simple way to prevent neural networks from overfitting
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Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 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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Face recognition vendor test (frvt) - performance of automated gender classification algorithms, 2015-04-20 2015
Ngan, M. and Grother, P · 2015
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2016
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Collell, G., Prelec, D., and Patil, K. R · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Price, E., and Srebro, N · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Price, E., and Srebro, N · 2016
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Data decisions and theoretical implications when adversarially learning fair representations, 2017
Beutel, A., Chen, J., Zhao, Z., and Chi, E. H · 2017
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A reductions approach to fair classification, 2018
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H · 2018
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Equity of attention: Amortizing individual fairness in rankings
Biega, A. J., Gummadi, K. P., and Weikum, G · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Measuring and mitigating unintended bias in text classification
Dixon, L., Li, J., Sorensen, J., Thain, N., and Vasserman, L · 2018
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Empirical risk minimization under fairness constraints
Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., and Pontil, M · 2018
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Non-discriminatory machine learning through convex fairness criteria
Goel, N., Yaghini, M., and Faltings, B · 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 adversarially fair and transferable representations, 2018
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
Fairness constraints: A flexible approach for fair classification
Zafar, M. B., Valera, I., Gomez-Rodriguez, M., and Gummadi, K. P · 2019
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Fairtorch
Akihiko Fukuchi, Yoko Yabe, M. S · 2020
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Do we need zero training loss after achieving zero training error?
Ishida, T., Yamane, I., Sakai, T., Niu, G., and Sugiyama, M · 2020
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Wasserstein fair classification
Jiang, R., Pacchiano, A., Stepleton, T., Jiang, H., and Chiappa, S · 2020
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Fairness without demographics through adversarially reweighted learning, 2020
Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., and Chi, E. H · 2020
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Martinez, N., Bertran, M., and Sapiro, G · 2020
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Inclusivefacenet: Improving face attribute detection with race and gender diversity, 2018
Ryu, H. J., Adam, H., and Mitchell, M · 2018
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Fairness of exposure in rankings
Singh, A. and Joachims, T · 2018
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Achieving fairness through adversarial learning: an application to recidivism prediction, 2018
Wadsworth, C., Vera, F., and Piech, C · 2018
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Mitigating unwanted biases with adversarial learning, 2018
Zhang, B. H., Lemoine, B., and Mitchell, M · 2018
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Putting fairness principles into practice: Challenges, metrics, and improvements
Beutel, A., Chen, J., Doshi, T., Qian, H., Woodruff, A., Luu, C., Kreitmann, P., Bischof, J., and Chi, E. H · 2019
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Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
Chzhen, E., Denis, C., Hebiri, M., Oneto, L., and Pontil, M · 2019
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Deep double descent: Where bigger models and more data hurt
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I · 2020
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Intra-processing methods for debiasing neural networks, 2020
Savani, Y., White, C., and Govindarajulu, N. S · 2020
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We need fairness and explainability in algorithmic hiring
Schumann, C., Foster, J. S., Mattei, N., and Dickerson, J. P · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation, 2020
Wang, Z., Qinami, K., Karakozis, I. C., Genova, K., Nair, P., Hata, K., and Russakovsky, O · 2020
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Optimized score transformation for fair classification
Wei, D., Ramamurthy, K. N., and Calmon, F · 2020
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Implicit gradient regularization
Barrett, D. and Dherin, B · 2021
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Technical challenges for training fair neural networks, 2021
Cherepanova, V., Nanda, V., Goldblum, M., Dickerson, J. P., and Goldstein, T · 2021
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Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
Karkkainen, K. and Joo, J · 2021
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Fnnc: Achieving fairness through neural networks
Padala, M. and Gujar, S · 2021
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On the origin of implicit regularization in stochastic gradient descent
Smith, S. L., Dherin, B., Barrett, D., and De, S · 2021
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