Fairness without Demographics through Adversarially Reweighted Learning

23 June 2020

Papers citing "Fairness without Demographics through Adversarially Reweighted Learning"

31 / 81 papers shown

Title
BARACK: Partially Supervised Group Robustness With Guarantees N. Sohoni Maziar Sanjabi Nicolas Ballas Aditya Grover Shaoliang Nie Hamed Firooz Christopher Ré OOD 24 24 0 31 Dec 2021
Measure and Improve Robustness in NLP Models: A Survey Xuezhi Wang Haohan Wang Diyi Yang 139 131 0 15 Dec 2021
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective Steven Euijong Whang Yuji Roh Hwanjun Song Jae-Gil Lee 27 326 0 13 Dec 2021
Learning Invariant Representations with Missing Data Mark Goldstein J. Jacobsen O. Chau A. Saporta A. Puli Rajesh Ranganath Andrew C. Miller OOD 25 5 0 01 Dec 2021
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features Haohan Wang Zeyi Huang Hanlin Zhang Yong Jae Lee Eric P. Xing OOD 138 16 0 05 Nov 2021
Modeling Techniques for Machine Learning Fairness: A Survey Mingyang Wan Daochen Zha Ninghao Liu Na Zou SyDa FaML 34 36 0 04 Nov 2021
Sample Selection for Fair and Robust Training Yuji Roh Kangwook Lee Steven Euijong Whang Changho Suh 21 61 0 27 Oct 2021
Boosted CVaR Classification Runtian Zhai Chen Dan A. Suggala Zico Kolter Pradeep Ravikumar 16 16 0 26 Oct 2021
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach Zhao Wang Kai Shu A. Culotta OOD 21 14 0 03 Oct 2021
A survey on datasets for fairness-aware machine learning Tai Le Quy Arjun Roy Vasileios Iosifidis Wenbin Zhang Eirini Ntoutsi FaML 11 241 0 01 Oct 2021
A Fairness Analysis on Private Aggregation of Teacher Ensembles Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 21 12 0 17 Sep 2021
Balancing out Bias: Achieving Fairness Through Balanced Training Xudong Han Timothy Baldwin Trevor Cohn 26 39 0 16 Sep 2021
Fairness without the sensitive attribute via Causal Variational Autoencoder Vincent Grari Sylvain Lamprier Marcin Detyniecki 24 27 0 10 Sep 2021
Towards Out-Of-Distribution Generalization: A Survey Jiashuo Liu Zheyan Shen Yue He Xingxuan Zhang Renzhe Xu Han Yu Peng Cui CML OOD 69 519 0 31 Aug 2021
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations Stephen R. Pfohl Haoran Zhang Yizhe Xu Agata Foryciarz Marzyeh Ghassemi N. Shah OOD 31 22 0 27 Aug 2021
Estimation of Fair Ranking Metrics with Incomplete Judgments Ömer Kirnap Fernando Diaz Asia J. Biega Michael D. Ekstrand Ben Carterette Emine Yilmaz 32 37 0 11 Aug 2021
Multiaccurate Proxies for Downstream Fairness Emily Diana Wesley Gill Michael Kearns K. Kenthapadi Aaron Roth Saeed Sharifi-Malvajerdi 35 21 0 09 Jul 2021
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models G. dÉon Jason dÉon J. R. Wright Kevin Leyton-Brown 33 74 0 01 Jul 2021
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations A. Puli Lily H. Zhang Eric K. Oermann Rajesh Ranganath OOD OODD 27 48 0 29 Jun 2021
DORO: Distributional and Outlier Robust Optimization Runtian Zhai Chen Dan J. Zico Kolter Pradeep Ravikumar 33 60 0 11 Jun 2021
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective Flavien Prost Pranjal Awasthi Nicholas Blumm A. Kumthekar Trevor Potter Li Wei Xuezhi Wang Ed H. Chi Jilin Chen Alex Beutel 50 15 0 20 May 2021
Technical Challenges for Training Fair Neural Networks Valeriia Cherepanova V. Nanda Micah Goldblum John P. Dickerson Tom Goldstein FaML 25 22 0 12 Feb 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 24 30 0 12 Feb 2021
Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification Maliha Tashfia Islam Anna Fariha A. Meliou Babak Salimi FaML 30 25 0 18 Jan 2021
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems N. Sohoni Jared A. Dunnmon Geoffrey Angus Albert Gu Christopher Ré 30 243 0 25 Nov 2020
Minimax Group Fairness: Algorithms and Experiments Emily Diana Wesley Gill Michael Kearns K. Kenthapadi Aaron Roth FaML FedML 22 23 0 05 Nov 2020
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness Mckane Andrus Elena Spitzer Jeffrey Brown Alice Xiang 32 126 0 30 Oct 2020
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning Pieter Delobelle Paul Temple Gilles Perrouin Benoit Frénay P. Heymans Bettina Berendt AAML FaML 27 14 0 14 May 2020
Robust Optimization for Fairness with Noisy Protected Groups S. Wang Wenshuo Guo Harikrishna Narasimhan Andrew Cotter Maya R. Gupta Michael I. Jordan NoLa 27 118 0 21 Feb 2020
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 195 742 0 13 Dec 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 236 676 0 17 Feb 2018