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In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream.
Learning factorial codes by predictability minimization
Schmidhuber, J · 1992
Earlier work this paper cites.
Domain adaptation with structural correspondence learning
Blitzer, J., McDonald, R., and Pereira, F · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., and Smola, A. J · 2007
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
Earlier work this paper cites.
Elements of information theory
Cover, T. M. and Thomas, J. A · 2012
Earlier work this paper cites.
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J · 2012
Earlier work this paper cites.
Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
Earlier work this paper cites.
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Discrimination-and privacy-aware patterns
Hajian, S., Domingo-Ferrer, J., Monreale, A., Pedreschi, D., and Giannotti, F · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Censoring representations with an adversary
Edwards, H. and Storkey, A · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., Srebro, N., et al · 2016
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