Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

12 June 2019

Papers citing "Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification"

9 / 59 papers shown

Title
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference Disi Ji Padhraic Smyth M. Steyvers 39 45 0 19 Oct 2020
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 37 49 0 25 Sep 2020
Fairness Constraints in Semi-supervised Learning Tao Zhang Tianqing Zhu Mengde Han Jing Li Wanlei Zhou Philip S. Yu FaML 6 7 0 14 Sep 2020
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm Kirtan Padh Diego Antognini Emma Lejal Glaude Boi Faltings C. Musat FaML 24 30 0 09 Sep 2020
Review of Mathematical frameworks for Fairness in Machine Learning E. del Barrio Paula Gordaliza Jean-Michel Loubes FaML FedML 15 39 0 26 May 2020
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh 24 78 0 24 Feb 2020
Optimized Score Transformation for Consistent Fair Classification Dennis L. Wei Karthikeyan N. Ramamurthy Flavio du Pin Calmon 24 15 0 31 May 2019
A statistical framework for fair predictive algorithms K. Lum J. Johndrow FaML 179 105 0 25 Oct 2016
High-dimensional generalized linear models and the lasso Sara van de Geer 201 749 0 04 Apr 2008