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Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms.
Regularized learning for domain adaptation under label shifts
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A survey on bias and fairness in machine learning
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Fair Predictors under Distribution Shift
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Information-theoretical optimization techniques
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Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
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Convex optimization
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Game Theory, Maximum Entropy, Minimum Discrepancy, and Robust Bayesian Decision Theory
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Applying data mining to predict college admissions yield: A case study
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Domain adaptation for statistical classifiers
Daume III, H.; and Marcu, D. 2006 · 2006
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Analysis of representations for domain adaptation
Ben-David, S.; Blitzer, J.; Crammer, K.; and Pereira, F. 2007 · 2007
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Covariate shift adaptation by importance weighted cross validation
Sugiyama, M.; Krauledat, M.; and Müller, K.-R. 2007 · 2007
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Learning bounds for domain adaptation
Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F.; and Wortman, J. 2008 · 2008
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Discrimination-aware data mining
Pedreshi, D.; Ruggieri, S.; and Turini, F. 2008 · 2008
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Building classifiers with independency constraints
Calders, T.; Kamiran, F.; and Pechenizkiy, M. 2009 · 2009
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Covariate shift by kernel mean matching
Gretton, A.; Smola, A.; Huang, J.; Schmittfull, M.; Borgwardt, K.; and Schölkopf, B. 2009 · 2009
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Learning bounds for importance weighting
Cortes, C.; Mansour, Y.; and Mohri, M. 2010 · 2010
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Fairness-aware learning through regularization approach
Kamishima, T.; Akaho, S.; and Sakuma, J. 2011 · 2011
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Data preprocessing techniques for classification without discrimination
Kamiran, F.; and Calders, T. 2012 · 2012
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On causal and anticausal learning
Schölkopf, B.; Janzing, D.; Peters, J.; Sgouritsa, E.; Zhang, K.; and Mooij, J. 2012 · 2012
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Predicting student performance by using data mining methods for classification
Kabakchieva, D. 2013 · 2013
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Big data, trying to build better workers
Lohr, S. 2013 · 2013
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Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C.; Blanchard, G.; and Handy, G. 2013 · 2013
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Learning Fair Representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
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Robust Classification Under Sample Selection Bias
Liu, A.; and Ziebart, B. 2014 · 2014
Recycling privileged learning and distribution matching for fairness
Quadrianto, N.; and Sharmanska, V. 2017 · 2017
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Learning Non-Discriminatory Predictors
Woodworth, B.; Gunasekar, S.; Ohannessian, M. I.; and Srebro, N. 2017 · 2017
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A Reductions Approach to Fair Classification
Agarwal, A.; Beygelzimer, A.; Dudík, M.; Langford, J.; and Wallach, H. M. 2018 · 2018
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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
Cotter, A.; Jiang, H.; Wang, S.; Narayan, T.; Gupta, M.; You, S.; and Sridharan, K. 2018 · 2018
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Obtaining fairness using optimal transport theory
Del Barrio, E.; Gamboa, F.; Gordaliza, P.; and Loubes, J.-M. 2018 · 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 · 2018
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Using big data for legal and law enforcement decisions: Testing the new tools
Moses, L. B.; and Chan, J. 2014 · 2014
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Certifying and removing disparate impact
Feldman, M.; Friedler, S. A.; Moeller, J.; Scheidegger, C.; and Venkatasubramanian, S. 2015 · 2015
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Fair learning in markovian environments
Jabbari, S.; Joseph, M.; Kearns, M.; Morgenstern, J.; and Roth, A. 2016 · 2016
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How we analyzed the COMPAS recidivism algorithm
Larson, J.; Mattu, S.; Kirchner, L.; and Angwin, J. 2016 · 2016
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Predicting the future—big data, machine learning, and clinical medicine
Obermeyer, Z.; and Emanuel, E. J. 2016 · 2016
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Non-discriminatory machine learning through convex fairness criteria
Goel, N.; Yaghini, M.; and Faltings, B. 2018 · 2018
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A survey of methods for explaining black box models
Guidotti, R.; Monreale, A.; Ruggieri, S.; Turini, F.; Giannotti, F.; and Pedreschi, D. 2018 · 2018
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Detecting and correcting for label shift with black box predictors
Lipton, Z. C.; Wang, Y.-X.; and Smola, A. 2018 · 2018
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Learning adversarially fair and transferable representations
Madras, D.; Creager, E.; Pitassi, T.; and Zemel, R. 2018 · 2018
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The cost of fairness in binary classification
Menon, A. K.; and Williamson, R. C. 2018 · 2018
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Fairness definitions explained
Verma, S.; and Rubin, J. 2018 · 2018
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FairGAN: Fairness-aware generative adversarial networks
Xu, D.; Yuan, S.; Zhang, L.; and Wu, X. 2018 · 2018
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Mitigating unwanted biases with adversarial learning
Zhang, B. H.; Lemoine, B.; and Mitchell, M. 2018 · 2018
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One-network Adversarial Fairness
Adel, T.; Valera, I.; Ghahramani, Z.; and Weller, A. 2019 · 2019
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Classification with fairness constraints: A meta-algorithm with provable guarantees
Celis, L. E.; Huang, L.; Keswani, V.; and Vishnoi, N. K. 2019 · 2019
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Improved Adversarial Learning for Fair Classification
Celis, L. E.; and Keswani, V. 2019 · 2019
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Noise-tolerant fair classification
Lamy, A.; Zhong, Z.; Menon, A. K.; and Verma, N. 2019 · 2019
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Equalized odds postprocessing under imperfect group information
Awasthi, P.; Kleindessner, M.; and Morgenstern, J. 2020 · 2020
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Fairness for Robust Log Loss Classification
Rezaei, A.; Fathony, R.; Memarrast, O.; and Ziebart, B. 2020 · 2020
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Yang, K.; Qinami, K.; Fei-Fei, L.; Deng, J.; and Russakovsky, O. 2020 · 2020
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