Learning Fair Representations via an Adversarial Framework

Learning Fair Representations via an Adversarial Framework

30 April 2019

Rui Feng

Papers citing "Learning Fair Representations via an Adversarial Framework"

11 / 11 papers shown

Title
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics Insung Kong Kunwoong Kim Yongdai Kim FaML 32 1 0 09 May 2025
Survey of Generative Methods for Social Media Analysis Stan Matwin Aristides Milios P. Prałat Amílcar Soares Franccois Théberge 27 3 0 13 Dec 2021
Latent Space Smoothing for Individually Fair Representations Momchil Peychev Anian Ruoss Mislav Balunović Maximilian Baader Martin Vechev FaML 36 19 0 26 Nov 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 196 0 12 Jul 2021
Fair Normalizing Flows Mislav Balunović Anian Ruoss Martin Vechev AAML 19 36 0 10 Jun 2021
Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination Tao Zhang Tianqing Zhu Jing Li Mengde Han Wanlei Zhou Philip S. Yu FaML 29 49 0 25 Sep 2020
Learning Certified Individually Fair Representations Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 15 92 0 24 Feb 2020
Deep Gamblers: Learning to Abstain with Portfolio Theory Liu Ziyin Zhikang T. Wang Paul Pu Liang Ruslan Salakhutdinov Louis-Philippe Morency Masahito Ueda 23 110 0 29 Jun 2019
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 742 0 13 Dec 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 675 0 17 Feb 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,090 0 24 Oct 2016