no code implementations • 28 Apr 2023 • Dangxing Chen, Weicheng Ye
In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity.
no code implementations • 21 Sep 2022 • Dangxing Chen, Weicheng Ye, Jiahui Ye
As a recent trend, researchers tend to use more complex and advanced machine learning methods to improve the accuracy of the prediction.
no code implementations • 21 Sep 2022 • Dangxing Chen, Weicheng Ye
Empirical results demonstrate that generalized gloves of neural additive models provide optimal accuracy with the simplest architecture, allowing for a highly accurate, transparent, and explainable approach to machine learning.
no code implementations • 21 Sep 2022 • Dangxing Chen, Weicheng Ye
In the absence of compliance with regulatory requirements, even highly accurate machine learning methods are unlikely to be accepted by companies for credit scoring.
no code implementations • 26 Jan 2019 • Tianyu Wang, Weicheng Ye, Dawei Geng, Cynthia Rudin
Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains.