83 citations · 224 across the 27 of their papers we have counts for
27 papers
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…
Document Automation Architectures: Updated Survey in Light of Large Language Models
Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi +1
This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically…
Using Markov Boundary Approach for Interpretable and Generalizable Feature Selection
Anwesha Bhattacharyya, Yaqun Wang, Joel Vaughan +1
The perceived advantage of machine learning (ML) models is that they are flexible and can incorporate a large number of features. However, many of these are typically correlated or…
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…
Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation
Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithm…
Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights
Tianshu Feng, Zhipu Zhou, Joshi Tarun +1
There are many different methods in the literature for local explanation of machine learning results. However, the methods differ in their approaches and often do not provide same…