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Found 8 papers, 1 papers with code
Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models
In recent years, explainable machine learning methods have been very successful.
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.
Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance
Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society.
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.
Two-stage Modeling for Prediction with Confidence
For the potential distribution shift problem, we propose a novel two-stage model.
Out-of-Distribution Detection
Vocal Bursts Valence Prediction
A Note on QR-Based Model Reduction: Algorithm, Software, and Gravitational Wave Applications
While the proper orthogonal decomposition (POD) is optimal under certain norms it's also expensive to compute.
Distributed, Parallel, and Cluster Computing General Relativity and Quantum Cosmology Numerical Analysis
