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Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibility of fairness constraints with stochastic optimization.
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Discrimination aware classification for imbalanced datasets
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Latanya Sweeney · 2013
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Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
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Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Benjamin Fish, Jeremy Kun, and Adám D Lelkes · 2015
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Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
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Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Igal Sason and Sergio Verdú · 2016
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Yahav Bechavod and Katrina Ligett · 2017
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
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Fair kernel learning
Adrián Pérez-Suay, Valero Laparra, Gonzalo Mateo-García, Jordi Muñoz-Marí, Luis Gómez-Chova, and Gustau Camps-Valls · 2017
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On fairness and calibration
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Recycling privileged learning and distribution matching for fairness
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Robert Williamson and Aditya Menon · 2019
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Depeng Xu, Shuhan Yuan, and Xintao Wu · 2019
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Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, and Meisam Razaviyayn · 2020
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Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford · 2020
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A fair classifier using mutual information
Jaewoong Cho, Gyeongjo Hwang, and Changho Suh · 2020
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A fair classifier using kernel density estimation
Jaewoong Cho, Gyeongjo Hwang, and Changho Suh · 2020
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Novi Quadrianto and Viktoriia Sharmanska · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
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Facebook’s advertising platform: New attack vectors and the need for interventions
Irfan Faizullabhoy and Aleksandra Korolova · 2018
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Evgenii Chzhen and Nicolas Schreuder · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Learning unbiased representations via Rényi minimization
Vincent Grari, Oualid El Hajouji, Sylvain Lamprier, and Marcin Detyniecki · 2020
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Wasserstein fair classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, and Silvia Chiappa · 2020
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Fair decisions despite imperfect predictions
Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, and Isabel Valera · 2020
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Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi · 2020
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On gradient descent ascent for nonconvex-concave minimax problems
Tianyi Lin, Chi Jin, and Michael I. Jordan · 2020
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Stochastic recursive gradient descent ascent for stochastic nonconvex-strongly-concave minimax problems
Luo Luo, Haishan Ye, and Tony Zhang · 2020
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Fair learning with private demographic data
Hussein Mozannar, Mesrob Ohannessian, and Nathan Srebro · 2020
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Efficient search of first-order nash equilibria in nonconvex-concave smooth min-max problems
Dmitrii M Ostrovskii, Andrew Lowy, and Meisam Razaviyayn · 2020
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Fairness for robust log loss classification
Ashkan Rezaei, Rizal Fathony, Omid Memarrast, and Brian D Ziebart · 2020
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Fr-train: A mutual information-based approach to fair and robust training
Yuji Roh, Kangwook Lee, Steven Whang, and Changho Suh · 2020
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Fast fair regression via efficient approximations of mutual information
Daniel Steinberg, Alistair Reid, Simon O’Callaghan, Finnian Lattimore, Lachlan McCalman, and Tiberio Caetano · 2020
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A distributionally robust approach to fair classification
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, and Jose Blanchet · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Andrew Lowy and Meisam Razaviyayn · 2021
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A survey on bias and fairness in machine learning
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The complexity of nonconvex-strongly-concave minimax optimization
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Bia mitigation for machine learning classifiers: A comprehensive survey
Max Hort, Zhenpeng Chen, Jie M Zhang, Federica Sarro, and Mark Harman · 2022
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Towards fair classifiers without sensitive attributes: Exploring biases in related features
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