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School effectiveness data set
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Regularized multi-task learning
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Kernel Methods for Pattern Analysis
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Convex multi-task feature learning
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Discrimination-aware data mining
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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Wine Quality Data Set
P. Cortez · 2009
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Classifying without discriminating
F. Kamiran and T. Calders · 2009
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Transfer bounds for linear feature learning
A. Maurer · 2009
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Measuring discrimination in socially-sensitive decision records
D. Pedreschi, S. Ruggieri, and F. Turini · 2009
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Dataset Shift in Machine Learning
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence · 2009
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Communities and Crime Data Set
M. Redmond · 2009
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Optimal Transport Old and New
C. Villani · 2009
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Convex Analysis and Nonlinear Optimization: Theory and Examples
J. Borwein and A. S. Lewis · 2010
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Three naive bayes approaches for discrimination-free classification
T. Calders and S. Verwer · 2010
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Classification with no discrimination by preferential sampling
F. Kamiran and T. Calders · 2010
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Selecting the hypothesis space for improving the generalization ability of support vector machines
D. Anguita, A. Ghio, L. Oneto, and S. Ridella · 2011
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Rule protection for indirect discrimination prevention in data mining
S. Hajian, J. Domingo-Ferrer, and A. Martinez-Balleste · 2011
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Heritage Health Data Set
Heritage Provider Network · 2011
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Fairness-aware learning through regularization approach
T. Kamishima, S. Akaho, and J. Sakuma · 2011
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k-nn as an implementation of situation testing for discrimination discovery and prevention
B. T. Luong, S. Ruggieri, and F. Turini · 2011
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Handling conditional discrimination
I. Zliobaite, F. Kamiran, and T. Calders · 2011
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Information effect of entry into credit ratings market: The case of insurers’ ratings
Neil A Doherty, Anastasia V Kartasheva, and Richard D Phillips · 2012
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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A methodology for direct and indirect discrimination prevention in data mining
S. Hajian and J. Domingo-Ferrer · 2012
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Injecting discrimination and privacy awareness into pattern discovery
S. Hajian, A. Monreale, D. Pedreschi, J. Domingo-Ferrer, and F. Giannotti · 2012
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Data preprocessing techniques for classification without discrimination
F. Kamiran and T. Calders · 2012
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Decision theory for discrimination-aware classification
F. Kamiran, A. Karim, and X. Zhang · 2012
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Fairness-aware classifier with prejudice remover regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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Discriminatory decision policy aware classification
K. Mancuhan and C. Clifton · 2012
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Stop, Question and Frisk Data Set
New York Police Department · 2012
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Controlling attribute effect in linear regression
T. Calders, A. Karim, F. Kamiran, W. Ali, and X. Zhang · 2013
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The independence of fairness-aware classifiers
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2013
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Children in the public benefit system at risk of maltreatment: Identification via predictive modeling
R. Vaithianathan, T. Maloney, E. Putnam-Hornstein, and N. Jiang · 2013
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Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Student Performance Data Set
P. Cortez · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Generalization-based privacy preservation and discrimination prevention in data publishing and mining
S. Hajian, J. Domingo-Ferrer, and O. Farràs · 2014
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Practical lessons from predicting clicks on ads at facebook
X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, and J. Q. Candela · 2014
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Combating discrimination using Bayesian networks
K. Mancuhan and C. Clifton · 2014
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Bank Marketing Data Set
S. Moro, P. Cortez, and P. Rita · 2014
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Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence
Z. Papamitsiou and A. A. Economides · 2014
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Machine learning for targeted display advertising: Transfer learning in action
C. Perlich, B. Dalessandro, T. Raeder, O. Stitelman, and F. Provost · 2014
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Understanding Machine Learning: From Theory to Algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Diabetes 130-US hospitals for years 1999-2008 Data Set
B. Strack, J. P. DeShazo, C. Gennings, J. L. Olmo, S. Ventura, K. J. Cios, and J. N. Clore · 2014
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Students’ Academic Performance Data Set
E. A. Amrieh, T. Hamtini, and I. Aljarah · 2015
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Predicting credit risk in peer-to-peer lending: A neural network approach
A. Byanjankar, M. Heikkilä, and J. Mezei · 2015
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Censoring representations with an adversary
H. Edwards and A. Storkey · 2015
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Computational fairness: Preventing machine-learned discrimination, 2015
M. Feldman · 2015
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Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Fair boosting: a case study
B. Fish, J. Kun, and A. Lelkes · 2015
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Prediction with model-based neutrality
K. Fukuchi, T. Kamishima, and J. Sakuma · 2015
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Discrimination-and privacy-aware patterns
S. Hajian, J. Domingo-Ferrer, A. Monreale, D. Pedreschi, and F. Giannotti · 2015
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Machine learning applications in cancer prognosis and prediction
K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis · 2015
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CelebA Data Set
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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From parity to preference-based notions of fairness in classification
M. B. Zafar, I. Valera, M. Rodriguez, K. Gummadi, and A. Weller · 2017
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Matching code and law: Achieving algorithmic fairness with optimal transport
M. Zehlike, P. Hacker, and E. Wiedemann · 2017
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Achieving non-discrimination in data release
L. Zhang, Y. Wu, and X. Wu · 2017
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A causal framework for discovering and removing direct and indirect discrimination
L. Zhang, Y. Wu, and X. Wu · 2017
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Auditing black-box models for indirect influence
P. Adler, C Falk, S. A. Friedler, T. Nix, G. Rybeck, C. Scheidegger, B. Smith, and S. Venkatasubramanian · 2018
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A reductions approach to fair classification
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Discrimination-aware association rule mining for unbiased data analytics
L. Luo, W. Liu, I. Koprinska, and F. Chen · 2015
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Chicago Face Data Set
D. S. Ma, J. Correll, and B. Wittenbrink · 2015
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Risk assessment in social lending via random forests
M. Malekipirbazari and V. Aksakalli · 2015
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Tikhonov, ivanov and morozov regularization for support vector machine learning
L. Oneto, S. Ridella, and D. Anguita · 2015
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A learning analytics approach to correlate the academic achievements of students with interaction data from an educational simulator
M. Vahdat, L. Oneto, D. Anguita, M. Funk, and M. Rauterberg · 2015
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Iterative orthogonal feature projection for diagnosing bias in black-box models
J. Adebayo and L. Kagal · 2016
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A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach · 2018
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Unleashing linear optimizers for group-fair learning and optimization
D. Alabi, N. Immorlica, and A. T. Kalai · 2018
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When optimizing nonlinear objectives is no harder than linear objectives
D. Alabi, N. Immorlica, and A. T. Kalai · 2018
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Penalizing unfairness in binary classification
Y. Bechavod and K. Ligett · 2018
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Help wanted: An examination of hiring algorithms, equity, and bias
M. Bogen and A. Rieke · 2018
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A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
A. Chouldechova, E. Putnam-Hornstein, D. Benavides-Prado, O. Fialko, and R. Vaithianathan · 2018
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Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
A. Cotter, M. Gupta, H. Jiang, N. Srebro, K. Sridharan, S. Wang, B. Woodworth, and S. You · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. Van Den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger · 2018
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Empirical risk minimization under fairness constraints
M. Donini, L. Oneto, S. Ben-David, J. S. Shawe-Taylor, and M. Pontil · 2018
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Decoupled classifiers for group-fair and efficient machine learning
C. Dwork, N. Immorlica, A. T. Kalai, and M. D. M. Leiserson · 2018
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Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
V. Eubanks · 2018
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Equality constrained decision trees: For the algorithmic enforcement of group fairness
J. Fitzsimons, A. A. Ali, M. Osborne, and S. Roberts · 2018
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Online learning with an unknown fairness metric
S. Gillen, C. Jung, M. Kearns, and A. Roth · 2018
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Fairness without demographics in repeated loss minimization
T. B. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
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Fairness behind a veil of ignorance: A welfare analysis for automated decision making
H. Heidari, C. Ferrari, K. Gummadi, and A. Krause · 2018
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A moral framework for understanding of fair ml through economic models of equality of opportunity
H. Heidari, M. Loi, K. P. Gummadi, and A. Krause · 2018
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Discretion in hiring
M. Hoffman, L. B. Kahn, and D. Li · 2018
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Student Academics Performance Data Set
S. Hussain, N. A. Dahan, F. M. Ba-Alwib, and N. Ribata · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2018
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Fairness through computationally-bounded awareness
M. Kim, O. Reingold, and G. Rothblum · 2018
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Nonconvex optimization for regression with fairness constraints
J. Komiyama, A. Takeda, J. Honda, and H. Shimao · 2018
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Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. Zemel · 2018
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Predict responsibly: improving fairness and accuracy by learning to defer
D. Madras, T. Pitassi, and R. Zemel · 2018
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The cost of fairness in binary classification
A. K. Menon and R. C. Williamson · 2018
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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
S. Mitchell, E. Potash, and S. Barocas · 2018
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Fair inference on outcomes
R. Nabi and I. Shpitser · 2018
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Learning with complex loss functions and constraints
H. Narasimhan · 2018
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Fair forests: Regularized tree induction to minimize model bias
E. Raff, J. Sylvester, and S. Mills · 2018
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Trump’s catch-and-detain policy snares many who call the U.S. home, 2018
M. Rosenberg and R. Levinson · 2018
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Learning controllable fair representations
J. Song, P. Kalluri, A. Grover, S. Zhao, and S. Ermon · 2018
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A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices
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Fairness definitions explained
S. Verma and J. Rubin · 2018
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Invariant representations from adversarially censored autoencoders
Y. Wang, T. Koike-Akino, and D. Erdogmus · 2018
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Probably approximately metric-fair learning
G. Yona and G. Rothblum · 2018
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Mitigating unwanted biases with adversarial learning
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Loss-aversively fair classification
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National Longitudinal Surveys Of Youth Data Set
Bureau of Labor Statistics · 2019
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Path-specific counterfactual fairness
S. Chiappa · 2019
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A causal Bayesian networks viewpoint on fairness
S. Chiappa and W. S. Isaac · 2019
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Leveraging labeled and unlabeled data for consistent fair binary classification
E. Chzhen, H. Hebiri, C. Denis, L. Oneto, and M. Pontil · 2019
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Two-player games for efficient non-convex constrained optimization
A. Cotter, H. Jiang, and K. Sridharan · 2019
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P. Gordaliza, E. Del Barrio, G. Fabrice, and L. Jean-Michel · 2019
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Fair classification and social welfare
L. Hu and Y. Chen · 2019
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Wasserstein fair classification
R. Jiang, A. Pacchiano, T. Stepleton, H. Jiang, and S. Chiappa · 2019
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An algorithm for removing sensitive information: application to race-independent recidivism prediction
J. E. Johndrow and K. Lum · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
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Costs and benefits of fair representation learning
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Diversity in Faces Data Set
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Active fairness in algorithmic decision making
Alejandro Noriega-Campero, Michiel A Bakker, Bernardo Garcia-Bulle, and Alex’Sandy’ Pentland · 2019
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Taking advantage of multitask learning for fair classification
L. Oneto, M. Donini, A. Elders, and M. Pontil · 2019
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Learning fair and transferable representations
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General fair empirical risk minimization
L. Oneto, M. Donini, and M. Pontil · 2019
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Computational optimal transport
M. Peyré, G.and M. Cuturi · 2019
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Fairness risk measures
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Fairness constraints: A flexible approach for fair classification
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi · 2019
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A general approach to fairness with optimal transport
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