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An Empirical Study of Accuracy, Fairness, Explainability, Distributional
  Robustness, and Adversarial Robustness

An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

29 September 2021
Moninder Singh
Gevorg Ghalachyan
Kush R. Varshney
R. Bryant
ArXivPDFHTML

Papers citing "An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness"

4 / 4 papers shown
Title
Data Optimization in Deep Learning: A Survey
Data Optimization in Deep Learning: A Survey
Ou Wu
Rujing Yao
30
1
0
25 Oct 2023
Function Composition in Trustworthy Machine Learning: Implementation
  Choices, Insights, and Questions
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
17
1
0
17 Feb 2023
Navigating Ensemble Configurations for Algorithmic Fairness
Navigating Ensemble Configurations for Algorithmic Fairness
Michael Feffer
Martin Hirzel
Samuel C. Hoffman
Kiran Kate
Parikshit Ram
Avraham Shinnar
FedML
FaML
16
0
0
11 Oct 2022
An Empirical Study of Modular Bias Mitigators and Ensembles
An Empirical Study of Modular Bias Mitigators and Ensembles
Michael Feffer
Martin Hirzel
Samuel C. Hoffman
Kiran Kate
Parikshit Ram
Avraham Shinnar
36
8
0
01 Feb 2022
1