41 citations · 57 across the 11 of their papers we have counts for
11 papers
Cross Spline Net and a Unified World
Linwei Hu, Ye Jin Choi, Vijayan N. Nair
In today's machine learning world for tabular data, XGBoost and fully connected neural network (FCNN) are two most popular methods due to their good model performance and convenien…
Monotone Tree-Based GAMI Models by Adapting XGBoost
Linwei Hu, Soroush Aramideh, Jie Chen +1
Recent papers have used machine learning architecture to fit low-order functional ANOVA models with main effects and second-order interactions. These GAMI (GAM + Interaction) model…
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…
Shapley Computations Using Surrogate Model-Based Trees
Zhipu Zhou, Jie Chen, Linwei Hu
Shapley-related techniques have gained attention as both global and local interpretation tools because of their desirable properties. However, their computation using conditional e…
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models
Linwei Hu, Jie Chen, Vijayan N. Nair
Low-order functional ANOVA (fANOVA) models have been rediscovered in the machine learning (ML) community under the guise of inherently interpretable machine learning. Explainable B…
Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study
Alice J. Liu, Arpita Mukherjee, Linwei Hu +2
This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algo…
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…
Supervised Machine Learning Techniques: An Overview with Applications to Banking
Linwei Hu, Jie Chen, Joel Vaughan +4
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Bo…
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
Linwei Hu, Jie Chen, Vijayan N. Nair +1
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive pe…
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)
Linwei Hu, Jie Chen, Vijayan N. Nair +1
Supervised Machine Learning (SML) algorithms such as Gradient Boosting, Random Forest, and Neural Networks have become popular in recent years due to their increased predictive per…
Saturated locally optimal designs under differentiable optimality criteria
Linwei Hu, Min Yang, John Stufken
We develop general theory for finding locally optimal designs in a class of single-covariate models under any differentiable optimality criterion. Yang and Stufken [Ann. Statist. 4…