FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

6 June 2022

Weijie J. Su

Papers citing "FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data"

30 / 30 papers shown

Title
Properties of fairness measures in the context of varying class imbalance and protected group ratios D. Brzezinski Julia Stachowiak Jerzy Stefanowski Izabela Szczech R. Susmaga Sofya Aksenyuk Uladzimir Ivashka Oleksandr Yasinskyi 241 6 0 13 Nov 2024
Specification Overfitting in Artificial Intelligence Benjamin Roth Pedro Henrique Luz de Araujo Yuxi Xia Saskia Kaltenbrunner Christoph Korab 207 1 0 13 Mar 2024
HappyMap: A Generalized Multi-calibration Method Zhun Deng Cynthia Dwork Linjun Zhang 228 19 0 08 Mar 2023
Reinforcement Learning with Stepwise Fairness Constraints Zhun Deng He Sun Zhiwei Steven Wu Linjun Zhang David C. Parkes FaML OffRL 73 11 0 08 Nov 2022
An Unconstrained Layer-Peeled Perspective on Neural Collapse Wenlong Ji Yiping Lu Yiliang Zhang Zhun Deng Weijie J. Su 188 87 0 06 Oct 2021
The Power of Contrast for Feature Learning: A Theoretical Analysis Wenlong Ji Zhun Deng Ryumei Nakada James Zou Linjun Zhang SSL 115 53 0 06 Oct 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 87 54 0 18 Jun 2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization Ganesh Ramachandra Kini Orestis Paraskevas Samet Oymak Christos Thrampoulidis 103 96 0 02 Mar 2021
Technical Challenges for Training Fair Neural Networks Valeriia Cherepanova V. Nanda Micah Goldblum John P. Dickerson Tom Goldstein FaML 54 22 0 12 Feb 2021
Minimax Pareto Fairness: A Multi Objective Perspective Natalia Martínez Martín Bertrán Guillermo Sapiro FaML 79 197 0 03 Nov 2020
Towards Understanding the Dynamics of the First-Order Adversaries Zhun Deng Hangfeng He Jiaoyang Huang Weijie J. Su AAML 44 11 0 20 Oct 2020
How Does Mixup Help With Robustness and Generalization? Linjun Zhang Zhun Deng Kenji Kawaguchi Amirata Ghorbani James Zou AAML 96 252 0 09 Oct 2020
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients Jing An Lexing Ying Yuhua Zhu 110 40 0 28 Sep 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 131 338 0 23 Jun 2020
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations Zhun Deng Frances Ding Cynthia Dwork Rachel Hong Giovanni Parmigiani Prasad Patil Pragya Sur OOD FaML 61 30 0 20 Jun 2020
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data Zhun Deng Linjun Zhang Amirata Ghorbani James Zou 78 32 0 15 Jun 2020
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 195 383 0 09 May 2020
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao Colin Wei Adrien Gaidon Nikos Arechiga Tengyu Ma 131 1,609 0 18 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 255 773 0 13 Dec 2018
What is the Effect of Importance Weighting in Deep Learning? Jonathon Byrd Zachary Chase Lipton 108 465 0 08 Dec 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 164 797 0 30 Apr 2018
A Reductions Approach to Fair Classification Alekh Agarwal A. Beygelzimer Miroslav Dudík John Langford Hanna M. Wallach FaML 230 1,105 0 06 Mar 2018
Empirical Risk Minimization under Fairness Constraints Michele Donini L. Oneto Shai Ben-David John Shawe-Taylor Massimiliano Pontil FaML 78 445 0 23 Feb 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 384 685 0 17 Feb 2018
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang Erik Learned-Miller Andrew McCallum 92 354 0 24 Apr 2017
Large-Margin Softmax Loss for Convolutional Neural Networks Weiyang Liu Yandong Wen Zhiding Yu Meng Yang CVBM 84 1,457 0 07 Dec 2016
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 351 4,636 0 10 Nov 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 236 4,341 0 07 Oct 2016
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 208 1,996 0 11 Dec 2014
Deep Learning Face Attributes in the Wild Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang CVBM 253 8,429 0 28 Nov 2014