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Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender).
Learning from noisy examples
Angluin, D. and Laird, P · 1988
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Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
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Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection
Chan, P. K. and Stolfo, S. J · 1998
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PAC Learning from Positive Statistical queries, 1998
Denis, F · 1998
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national longitudinal bar passage study, 1998
Wightman, L · 1998
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Differential privacy
Dwork, C · 2006
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Learning classifiers from only positive and unlabeled data
Elkan, C. and Noto, K · 2008
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Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., and Turini, F · 2008
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Learning classifiers without negative examples: A reduction approach
Zhang, D. and Lee, W. S · 2008
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Building classifiers with independency constraints
Calders, T., Kamiran, F., and Pechenizkiy, M · 2009
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Presence-Only Data and the {EM} Algorithm
Ward, G., Hastie, T., Barry, S., Elith, J., and Leathwick, J. R · 2009
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The balanced accuracy and its posterior distribution
Brodersen, K. H., Ong, C. S., Stephan, K. E., and Buhmann, J. M · 2010
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Three naive bayes approaches for discrimination-free classification
Calders, T. and Verwer, S · 2010
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Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. S · 2011
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On the statistical consistency of algorithms for binary classification under class imbalance
Menon, A. K., Narasimhan, H., Agarwal, S., and Chawla, S · 2013
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Learning with noisy labels
Natarajan, N., Tewari, A., Dhillon, I. S., and Ravikumar, P · 2013
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Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
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Efficient learning of linear separators under bounded noise
Awasthi, P., Balcan, M.-F., Haghtalab, N., and Urner, R · 2015
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Computational fairness: Preventing machine-learned discrimination
Feldman, M · 2015
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The variational fair auto encoder
Louizos, C., Swersky, K., Li, Y., Welling, M., and Zemel, R · 2015
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Learning from corrupted binary labels via class-probability estimation
Menon, A. K., van Rooyen, B., Ong, C. S., and Williamson, R. C · 2015
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Machine Learning via Transitions
van Rooyen, B · 2015
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Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2016
Cited alongside, same era.
A statistical framework for fair predictive algorithms
Lum, K. and Johndrow, J. E · 2016
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Obtaining fairness using optimal transport theory
del Barrio, E., Gamboa, F., Gordaliza, P., and Loubes, J.-M · 2018
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Empirical risk minimization under fairness constraints
Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J. S., and Pontil, M · 2018
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Decoupled classifiers for group-fair and efficient machine learning
Dwork, C., Immorlica, N., Kalai, A. T., and Leiserson, M · 2018
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Gupta, M. R., Cotter, A., Fard, M. M., and Wang, S · 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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Mixture proportion estimation via kernel embedding of distributions
Ramaswamy, H. G., Scott, C., and Tewari, A · 2016
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Calmon, F., Wei, D., Vinzamuri, B., Natesan Ramamurthy, K., and Varshney, K. R · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru, D. and Karra Taniskidou, E · 2017
Cited alongside, same era.
Recovering true classifier performance in positive-unlabeled learning
Jain, S., White, M., and Radivojac, P · 2017
Cited alongside, same era.
Johndrow, J. E. and Lum, K · 2017
Cited alongside, same era.
Positive-unlabeled learning with non-negative risk estimator
Kiryo, R., Niu, G., du Plessis, M., and Sugiyama, M · 2017
Cited alongside, same era.
Differentially private fair learning
Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifi-Malvajerdi, S., and Ullman, J · 2018
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Blind justice: Fairness with encrypted sensitive attributes
Kilbertus, N., Gascon, A., Kusner, M., Veale, M., Gummadi, K., and Weller, A · 2018
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Fairness through computationally-bounded awareness
Kim, M. P., Reingold, O., and Rothblum, G. N · 2018
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ifair: Learning individually fair data representations for algorithmic decision making
Lahoti, P., Weikum, G., and P. Gummadi, K · 2018
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Does mitigating ml’s impact disparity require treatment disparity?
Lipton, Z., McAuley, J., and Chouldechova, A · 2018
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A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices
Speicher, T., Heidari, H., Grgic-Hlaca, N., Gummadi, K. P., Singla, A., Weller, A., and Zafar, M. B · 2018
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A theory of learning with corrupted labels
van Rooyen, B. and Williamson, R. C · 2018
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Equalized odds postprocessing under imperfect group information, 2019
Awasthi, P., Kleindessner, M., and Morgenstern, J · 2019
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On symmetric losses for learning from corrupted labels
Charoenphakdee, N., Lee, J., and Sugiyama, M · 2019
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A moral framework for understanding of fair ml through economic models of equality of opportunity
Heidari, H., Loi, M., Gummadi, K. P., and Krause, A · 2019
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Decontamination of mutual contamination models
Katz-Samuels, J., Blanchard, G., and Scott, C · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
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Fair learning with private data
Mozannar, H., Ohannessian, M. I., and Srebro, N · 2019
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Fairness risk measures
Williamson, R. C. and Menon, A. K · 2019
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