To be Robust or to be Fair: Towards Fairness in Adversarial Training

13 October 2020

Xiaorui Liu

Papers citing "To be Robust or to be Fair: Towards Fairness in Adversarial Training"

32 / 32 papers shown

Title
Long-tailed Adversarial Training with Self-Distillation Seungju Cho Hongsin Lee Changick Kim AAML TTA 230 0 0 09 Mar 2025
Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers Huan Tian Guangsheng Zhang Bo Liu Tianqing Zhu Ming Ding Wanlei Zhou 53 0 0 08 Mar 2025
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training Tejaswini Medi Steffen Jung M. Keuper AAML 44 3 0 30 Oct 2024
The Pursuit of Fairness in Artificial Intelligence Models: A Survey Tahsin Alamgir Kheya Mohamed Reda Bouadjenek Sunil Aryal 36 8 0 26 Mar 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 42 1 0 29 Jun 2023
Causality-Aided Trade-off Analysis for Machine Learning Fairness Zhenlan Ji Pingchuan Ma Shuai Wang Yanhui Li FaML 34 7 0 22 May 2023
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems Nicolò Pagan Joachim Baumann Ezzat Elokda Giulia De Pasquale S. Bolognani Anikó Hannák 50 23 0 10 May 2023
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
Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing Walid Hariri AI4MH LM&MA 33 85 0 27 Mar 2023
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities Zhuqing Liu Xin Zhang Songtao Lu Jia-Wei Liu 40 7 0 05 Mar 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
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition Luke E. Richards Edward Raff Cynthia Matuszek AAML 16 2 0 17 Feb 2023
Do Neural Networks Generalize from Self-Averaging Sub-classifiers in the Same Way As Adaptive Boosting? Michael Sun Peter Chatain AI4CE 29 0 0 14 Feb 2023
Fairness Increases Adversarial Vulnerability Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 34 6 0 21 Nov 2022
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues Canyu Chen Haoran Wang Matthew A. Shapiro Yunyu Xiao Fei Wang Kai Shu 22 12 0 10 Nov 2022
Fairness-aware Regression Robust to Adversarial Attacks Yulu Jin Lifeng Lai FaML OOD 29 4 0 04 Nov 2022
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting Peng-Fei Hou Jie Han Xingyu Li AAML OOD 23 11 0 26 Oct 2022
Improving Robust Fairness via Balance Adversarial Training Chunyu Sun Chenye Xu Chengyuan Yao Siyuan Liang Yichao Wu Ding Liang XiangLong Liu Aishan Liu 23 11 0 15 Sep 2022
Class-Level Logit Perturbation Mengyang Li Fengguang Su O. Wu Tianjin University AAML 31 3 0 13 Sep 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM Chulin Xie Pin-Yu Chen Qinbin Li Arash Nourian Ce Zhang Bo Li FedML 38 16 0 20 Jul 2022
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning Damien Dablain Bartosz Krawczyk Nitesh V. Chawla FaML 26 19 0 13 Jul 2022
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks Kiarash Mohammadi Aishwarya Sivaraman G. Farnadi 25 5 0 01 Jun 2022
Pruning has a disparate impact on model accuracy Cuong Tran Ferdinando Fioretto Jung-Eun Kim Rakshit Naidu 41 38 0 26 May 2022
Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems Mostafa M. Mohamed Björn W. Schuller 19 5 0 02 Feb 2022
Can Adversarial Training Be Manipulated By Non-Robust Features? Lue Tao Lei Feng Hongxin Wei Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 104 16 0 31 Jan 2022
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 119 356 0 04 Oct 2021
Imbalanced Adversarial Training with Reweighting Wentao Wang Han Xu Xiaorui Liu Yaxin Li B. Thuraisingham Jiliang Tang 37 16 0 28 Jul 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 196 0 12 Jul 2021
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs Lorena Qendro Sangwon Ha R. D. Jong Partha P. Maji AAML FedML MQ 15 7 0 13 May 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
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016