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FAIR: Fair Adversarial Instance Re-weighting

FAIR: Fair Adversarial Instance Re-weighting

Neurocomputing (Neurocomputing), 2020
15 November 2020
Andrija Petrović
Mladen Nikolic
Sandro Radovanović
Boris Delibavsić
Milovs Jovanović
    FaMLAAML
ArXiv (abs)PDFHTML

Papers citing "FAIR: Fair Adversarial Instance Re-weighting"

14 / 14 papers shown
Learning Fair Representations with Kolmogorov-Arnold Networks
Learning Fair Representations with Kolmogorov-Arnold Networks
Amisha Priyadarshini
Sergio Gago Masagué
FaML
602
0
0
14 Nov 2025
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated DistributionAAAI Conference on Artificial Intelligence (AAAI), 2025
Songran Bai
Yuheng Ji
Yue Liu
Xingwei Zhang
Xiaolong Zheng
D. Zeng
315
3
0
01 Apr 2025
The Power of Few: Accelerating and Enhancing Data Reweighting with
  Coreset Selection
The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset SelectionIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024
Mohammad Jafari
Yimeng Zhang
Yihua Zhang
Sijia Liu
395
4
0
18 Mar 2024
Evolutionary Reinforcement Learning: A Systematic Review and Future
  Directions
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
Y. Lin
Fan Lin
Guorong Cai
Hong Chen
Lixin Zou
Pengcheng Wu
239
9
0
20 Feb 2024
Data Optimization in Deep Learning: A Survey
Data Optimization in Deep Learning: A SurveyIEEE Transactions on Knowledge and Data Engineering (TKDE), 2023
Ou Wu
Rujing Yao
368
6
0
25 Oct 2023
A Critical Survey on Fairness Benefits of Explainable AI
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck
Jakob Schoeffer
Maria De-Arteaga
Niklas Kühl
644
44
0
15 Oct 2023
Towards Better Fairness-Utility Trade-off: A Comprehensive
  Measurement-Based Reinforcement Learning Framework
Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework
Simiao Zhang
Jitao Bai
Menghong Guan
Yihao Huang
Yueling Zhang
Jun Sun
G. Pu
FaML
226
1
0
21 Jul 2023
Pushing the Accuracy-Group Robustness Frontier with Introspective
  Self-play
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
J. Liu
Krishnamurthy Dvijotham
Jihyeon Janel Lee
Quan Yuan
Martin Strobel
Balaji Lakshminarayanan
Deepak Ramachandran
259
5
0
11 Feb 2023
Interpreting Unfairness in Graph Neural Networks via Training Node
  Attribution
Interpreting Unfairness in Graph Neural Networks via Training Node AttributionAAAI Conference on Artificial Intelligence (AAAI), 2022
Yushun Dong
Song Wang
Jing Ma
Ninghao Liu
Jundong Li
245
31
0
25 Nov 2022
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing (JRC), 2022
Max Hort
Zhenpeng Chen
Jie M. Zhang
Mark Harman
Federica Sarro
FaMLAI4CE
423
256
0
14 Jul 2022
The Fairness of Credit Scoring Models
The Fairness of Credit Scoring ModelsSocial Science Research Network (SSRN), 2021
Christophe Hurlin
C. Pérignon
Sébastien Saurin
FaML
223
43
0
20 May 2022
FORML: Learning to Reweight Data for Fairness
FORML: Learning to Reweight Data for Fairness
Bobby Yan
Skyler Seto
N. Apostoloff
FaML
304
16
0
03 Feb 2022
Algorithm Fairness in AI for Medicine and Healthcare
Algorithm Fairness in AI for Medicine and Healthcare
Richard J. Chen
Tiffany Y. Chen
Jana Lipkova
Judy J. Wang
Drew F. K. Williamson
Ming Y. Lu
S. Sahai
Faisal Mahmood
FaML
326
54
0
01 Oct 2021
Fairness in Machine Learning: A Survey
Fairness in Machine Learning: A SurveyACM Computing Surveys (ACM CSUR), 2020
Simon Caton
C. Haas
FaML
760
841
0
04 Oct 2020
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