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20152021
most citedAn Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

4 citations · 7 across the 4 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cs.LG2019

Preservation of Anomalous Subgroups On Machine Learning Transformed Data

Samuel C. Maina, Reginald E. Bryant, William O. Goal +3

In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the mos…

cs.CR2019★ 2 cited

Analyzing Bias in Sensitive Personal Information Used to Train Financial Models

Reginald Bryant, Celia Cintas, Isaac Wambugu +2

Bias in data can have unintended consequences that propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can resul…