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We consider a fair representation learning perspective, where optimal predictors, on top of the data representation, are ensured to be invariant with respect to different sub-groups.
Block preconditioning for the conjugate gradient method
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Lsac national longitudinal bar passage study, 1998
Wightman, L. F · 1998
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Gradient-based optimization of hyperparameters
Bengio, Y · 2000
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Convex optimization
Boyd, S., Boyd, S. P., and Vandenberghe, L · 2004
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Bias and fairness in natural language processing
Chang, K.-W., Prabhakaran, V., and Ordonez, V · 2004
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Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 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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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, 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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Hyperparameter optimization with approximate gradient
Pedregosa, F · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Toxic comment classification challenge, 2018
Jigsaw · 2018
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Nonconvex optimization for regression with fairness constraints
Komiyama, J., Takeda, A., Honda, J., and Shimao, H · 2018
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Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., and Ermon, S · 2018
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Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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Mitigating unwanted biases with adversarial learning
Zhang, B. H., Lemoine, B., and Mitchell, M · 2018
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Fair regression: Quantitative definitions and reduction-based algorithms
Agarwal, A., Dudík, M., and Wu, Z. S · 2019
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Fair regression with wasserstein barycenters
Chzhen, E., Denis, C., Hebiri, M., Oneto, L., and Pontil, M · 2020
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Is your classifier actually biased? measuring fairness under uncertainty with bernstein bounds
Ethayarajh, K · 2020
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Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health
Fletcher, R. R., Nakeshimana, A., and Olubeko, O · 2020
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Wasserstein fair classification
Jiang, R., Pacchiano, A., Stepleton, T., Jiang, H., and Chiappa, S · 2020
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Null-sampling for interpretable and fair representations
Kehrenberg, T., Bartlett, M., Thomas, O., and Quadrianto, N · 2020
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Fairness and Machine Learning
Barocas, S., Hardt, M., and Narayanan, A · 2019
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Learning not to learn: Training deep neural networks with biased data
Kim, B., Kim, H., Kim, K., Kim, S., and Kim, J · 2019
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The implicit fairness criterion of unconstrained learning
Liu, L. T., Simchowitz, M., and Hardt, M · 2019
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On the fairness of disentangled representations
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., and Bachem, O · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S · 2019
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Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S., and Levine, S · 2019
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Raghavan, M., Barocas, S., Kleinberg, J., and Levy, K · 2020
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The risks of invariant risk minimization
Rosenfeld, E., Ravikumar, P., and Risteski, A · 2020
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Racial bias in pulse oximetry measurement
Sjoding, M. W., Dickson, R. P., Iwashyna, T. J., Gay, S. E., and Valley, T. S · 2020
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Iterative feature matching: Toward provable domain generalization with logarithmic environments
Chen, Y., Rosenfeld, E., Sellke, M., Ma, T., and Risteski, A · 2021
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Fair mixup: Fairness via interpolation
Chuang, C.-Y. and Mroueh, Y · 2021
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Environment inference for invariant learning
Creager, E., Jacobsen, J.-H., and Zemel, R · 2021
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Controllable guarantees for fair outcomes via contrastive information estimation
Gupta, U., Ferber, A., Dilkina, B., and Ver Steeg, G · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2021
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National longitudinal survey of youth, 2021
NLSY · 2021
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Problem with proof of Conditional expectation as best predictor
Online · 2022
Closest in time.
On the benefits of representation regularization in invariance based domain generalization
Shui, C., Wang, B., and Gagné, C · 2022
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