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How to address monotonicity for model risk management?
In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity.
Interpretable Selective Learning in Credit Risk
As a recent trend, researchers tend to use more complex and advanced machine learning methods to improve the accuracy of the prediction.
Generalized Gloves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance
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.
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring
In the absence of compliance with regulatory requirements, even highly accurate machine learning methods are unlikely to be accepted by companies for credit scoring.
Towards Practical Lipschitz Bandits
Stochastic Lipschitz bandit algorithms balance exploration and exploitation, and have been used for a variety of important task domains.
