83 citations · 308 across the 26 of their papers we have counts for
26 papers
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons
Linwei Hu, Vijayan N. Nair, Agus Sudjianto +2
In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, o…
Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization
Shijie Cui, Agus Sudjianto, Aijun Zhang +1
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling. However, their complex structure may lead to low ro…
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
Vijayan N. Nair, Tianshu Feng, Linwei Hu +3
When a financial institution declines an application for credit, an adverse action (AA) is said to occur. The applicant is then entitled to an explanation for the negative decision…
Designing Inherently Interpretable Machine Learning Models
Agus Sudjianto, Aijun Zhang
Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings. In…
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
Shaojie Xu, Joel Vaughan, Jie Chen +2
Although neural networks (NNs) with ReLU activation functions have found success in a wide range of applications, their adoption in risk-sensitive settings has been limited by the…
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Shaojie Xu, Joel Vaughan, Jie Chen +2
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling. It is an unsupervised learning techniq…