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Through the lens of information-theoretic reductions, we examine a reductions approach to fair optimization and learning where a black-box optimizer is used to learn a fair model for classification or regression.
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Frank McSherry and Kunal Talwar · 2007
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Alina Beygelzimer, John Langford, and Bianca Zadrozny · 2009
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Cynthia Dwork and Jing Lei · 2009
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Playing games with approximation algorithms
Sham M. Kakade, Adam Tauman Kalai, and Katrina Ligett · 2009
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f-divergence inequalities
Igal Sason and Sergio Verdú · 2016
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Multi-objective bandits: Optimizing the generalized gini index
Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, and Shie Mannor · 2017
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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On surrogate loss functions and f-divergences
XuanLong Nguyen, Martin J. Wainwright, and Michael I. Jordan · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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Theoretical Statistics: Topics for a Core Course
R.W. Keener · 2010
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Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel J. Hsu · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Optimization with sparsity-inducing penalties
Francis R. Bach, Rodolphe Jenatton, Julien Mairal, and Guillaume Obozinski · 2012
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The complexity of differential privacy
Salil P. Vadhan · 2017
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The u.s. census bureau adopts differential privacy
John M. Abowd · 2018
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A reductions approach to fair classification
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Unleashing linear optimizers for group-fair learning and optimization
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Gender shades: Intersectional accuracy disparities in commercial gender classification
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Privacy for all: Ensuring fair and equitable privacy protections
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Understanding database reconstruction attacks on public data
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Differentially private fair learning
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Blind justice: Fairness with encrypted sensitive attributes
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Learning with complex loss functions and constraints
Harikrishna Narasimhan · 2018
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Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2018
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Fair regression: Quantitative definitions and reduction-based algorithms
Alekh Agarwal, Miroslav Dudík, and Zhiwei Steven Wu · 2019
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The structure of optimal private tests for simple hypotheses
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The secret sharer: Evaluating and testing unintended memorization in neural networks
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High probability generalization bounds for uniformly stable algorithms with nearly optimal rate
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Time/accuracy tradeoffs for learning a relu with respect to gaussian marginals
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Multiclass performance metric elicitation
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Differentially private ordinary least squares
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