To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair
Training on Shared Models

To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models

29 February 2024

Suresh Venkatasubramanian

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Papers citing "To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models"

11 / 11 papers shown

Title
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning Cyrus Cousins FaML 107 27 0 29 Apr 2021
Adaptive Sampling for Minimax Fair Classification S. Shekhar Greg Fields Mohammad Ghavamzadeh T. Javidi FaML 110 37 0 01 Mar 2021
Minimax Pareto Fairness: A Multi Objective Perspective Natalia Martínez Martín Bertrán Guillermo Sapiro FaML 74 193 0 03 Nov 2020
Fairness without Demographics through Adversarially Reweighted Learning Preethi Lahoti Alex Beutel Jilin Chen Kang Lee Flavien Prost Nithum Thain Xuezhi Wang Ed H. Chi FaML 121 337 0 23 Jun 2020
Active Sampling for Min-Max Fairness Jacob D. Abernethy Pranjal Awasthi Matthäus Kleindessner Jamie Morgenstern Chris Russell Jie Zhang FaML 46 50 0 11 Jun 2020
To Split or Not to Split: The Impact of Disparate Treatment in Classification Hao Wang Hsiang Hsu Mario Díaz Flavio du Pin Calmon 71 23 0 12 Feb 2020
Accuracy comparison across face recognition algorithms: Where are we on measuring race bias? J. G. Cavazos P. Phillips Carlos D. Castillo Alice J. OrToole CVBM 51 169 0 16 Dec 2019
Distributionally Robust Language Modeling Yonatan Oren Shiori Sagawa Tatsunori B. Hashimoto Percy Liang OOD 70 173 0 04 Sep 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 568 14 0 23 Aug 2019
Why Is My Classifier Discriminatory? Irene Y. Chen Fredrik D. Johansson David Sontag FaML 69 396 0 30 May 2018
A Survey on Multi-Task Learning Yu Zhang Qiang Yang AIMat 605 2,235 0 25 Jul 2017