Robustness May Be at Odds with Fairness: An Empirical Study on
Class-wise Accuracy

Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy

26 October 2020

In So Kweon

Papers citing "Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy"

10 / 10 papers shown

Title
On the uncertainty principle of neural networks Jun-Jie Zhang Dong-xiao Zhang Jian-Nan Chen L. Pang Deyu Meng 57 2 0 17 Jan 2025
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training Tejaswini Medi Steffen Jung M. Keuper AAML 44 3 0 30 Oct 2024
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness Zongxiong Chen Jiahui Geng Derui Zhu Herbert Woisetschlaeger Qing Li Sonja Schimmler Ruben Mayer Chunming Rong DD 26 9 0 05 May 2023
Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects Conrad Sanderson David M. Douglas Qinghua Lu 43 12 0 17 Apr 2023
UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks Mahum Naseer B. Prabakaran Osman Hasan Muhammad Shafique 24 7 0 24 Feb 2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions Manish Nagireddy Moninder Singh Samuel C. Hoffman Evaline Ju K. Ramamurthy Kush R. Varshney 30 1 0 17 Feb 2023
Analysis and Applications of Class-wise Robustness in Adversarial Training Qi Tian Kun Kuang Ke Jiang Fei Wu Yisen Wang AAML 20 46 0 29 May 2021
Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction Ferhat Ozgur Catak Evren Çatak Murat Kuzlu Umit Cali Devrim Unal AAML 35 44 0 09 May 2021
Universal Adversarial Training with Class-Wise Perturbations Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 22 26 0 07 Apr 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 678 0 19 Oct 2020