Fetching the paper…
Reading the bibliography…
We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes.
A Software Package for Sequential Quadratic Programming
D. Kraft · 1988
Earlier work this paper cites.
Closing the Gap: A Guide to Equal Opportunity Lending
Boston Federal Reserve Bank · 1993
Earlier work this paper cites.
Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1994
Earlier work this paper cites.
Sphere packing numbers for subsets of the Boolean
David Haussler · 1995
Earlier work this paper cites.
A tutorial on support vector machines for pattern recognition
Christopher J. C. Burges · 1998
Earlier work this paper cites.
Introduction to Coding Theory
J. H. Van Lint · 1998
Earlier work this paper cites.
Shades of citizenship: Race and the census in modern politics
Melissa Nobles · 2000
Earlier work this paper cites.
Eliminating health disparities: Measurement and data needs
Ethnicity Data, Michele Ver Ploeg, and Edward Perrin · 2004
Earlier work this paper cites.
Adverse impact and test validation: A practitioner’s guide to valid and defensible employment testing
Dan Biddle · 2006
Earlier work this paper cites.
UCI machine learning repository
A. Asuncion and D.J. Newman · 2007
Earlier work this paper cites.
Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
Using the census bureau’s surname list to improve estimates of race/ethnicity and associated disparities
Marc N Elliott, Peter A Morrison, Allen Fremont, Daniel F McCaffrey, Philip Pantoja, and Nicole Lurie · 2009
Earlier work this paper cites.
Statistical models: theory and practice
David A Freedman · 2009
Earlier work this paper cites.
Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
Earlier work this paper cites.
Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
Earlier work this paper cites.
Fair lending: Comptroller’s Handbook
Comptroller · 2010
Earlier work this paper cites.
Geometric Approximation Algorithms
Sariel Har-peled · 2011
Earlier work this paper cites.
Matrix analysis
Roger A Horn and Charles R Johnson · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Earlier work this paper cites.
Tackling the problem of classification with noisy data using multiple classifier systems: Analysis of the performance and robustness
Jose A Saez, Mikel Galar, Julian Luengo, and Francisco Herrera · 2013
Cited alongside, same era.
Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
Cited alongside, same era.
Learning from corrupted binary labels via class-probability estimation
Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, and Bob Williamson · 2015
Cited alongside, same era.
A confidence-based approach for balancing fairness and accuracy
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes · 2016
Cited alongside, same era.
Satisfying real-world goals with dataset constraints
Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Later among the works it cites.
Understanding unequal gender classification accuracy from face images
Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, and Kush R. Varshney · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Later among the works it cites.
Classification with fairness constraints: A meta-algorithm with provable guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K. Vishnoi · 2019
Later among the works it cites.
The relationships between data, power, and justice in cscw research
Stevie Chancellor, Shion Guha, Jofish Kaye, Jen King, Niloufar Salehi, Sarita Schoenebeck, and Elizabeth Stowell · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gabriel Goh, Andrew Cotter, Maya R. Gupta, and Michael P. Friedlander · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Flávio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney · 2017
Cited alongside, same era.
Learning with confident examples: Rank pruning for robust classification with noisy labels
Curtis G Northcutt, Tailin Wu, and Isaac L Chuang · 2017
Cited alongside, same era.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Learning non-discriminatory predictors
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2017
Cited alongside, same era.
Fairness under unawareness: Assessing disparity when protected class is unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell · 2019
Later among the works it cites.
A comparative study of fairness-enhancing interventions in machine learning
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth · 2019
Later among the works it cites.
Obtaining fairness using optimal transport theory
Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, and Jean-Michel Loubes · 2019
Later among the works it cites.
Stable and fair classification
Lingxiao Huang and Nisheeth K. Vishnoi · 2019
Later among the works it cites.
Noise-tolerant fair classification
Alex Lamy, Ziyuan Zhong, Aditya K Menon, and Nakul Verma · 2019
Later among the works it cites.
Mathematical notions vs. human perception of fairness: A descriptive approach to fairness for machine learning
Megha Srivastava, Hoda Heidari, and Andreas Krause · 2019
Later among the works it cites.
Repairing without retraining: Avoiding disparate impact with counterfactual distributions
Hao Wang, Berk Ustun, and Flávio P. Calmon · 2019
Later among the works it cites.
Equalized odds postprocessing under imperfect group information
Pranjal Awasthi, Matthaus Kleindessner, and Jamie Morgenstern · 2020
Closest in time.
Ensuring fairness under prior probability shifts
Arpita Biswas and Suvam Mukherjee · 2020
Closest in time.
Recovering from biased data: Can fairness constraints improve accuracy?
Avrim Blum and Kevin Stangl · 2020
Closest in time.
Data preprocessing to mitigate bias: A maximum entropy based approach
L. Elisa Celis, Vijay Keswani, and Nisheeth K. Vishnoi · 2020
Closest in time.
Assessing algorithmic fairness with unobserved protected class using data combination
Nathan Kallus, Xiaojie Mao, and Angela Zhou · 2020
Closest in time.
Creating community-based tech policy: case studies, lessons learned, and what technologists and communities can do together
Hannah Sassaman, Jennifer Lee, Jenessa Irvine, and Shankar Narayan · 2020
Closest in time.
Robust optimization for fairness with noisy protected groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya R. Gupta, and Michael I. Jordan · 2020
Closest in time.