20 citations · 68 across the 9 of their papers we have counts for
9 papers
Inherently Interpretable Tree Ensemble Learning
Zebin Yang, Agus Sudjianto, Xiaoming Li +1
Tree ensemble models like random forests and gradient boosting machines are widely used in machine learning due to their excellent predictive performance. However, a high-performan…
PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
Agus Sudjianto, Aijun Zhang, Zebin Yang +2
PiML (read -ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with mac…
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
Yifeng Guo, Yu Su, Zebin Yang +1
In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a…
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
Agus Sudjianto, William Knauth, Rahul Singh +2
The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box" models without a…
Hyperparameter Optimization via Sequential Uniform Designs
Zebin Yang, Aijun Zhang
Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML). It is a challenging task as the response surfaces of hyperparameters are generall…
An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks
Zebin Yang, Hengtao Zhang, Agus Sudjianto +1
Network initialization is the first and critical step for training neural networks. In this paper, we propose a novel network initialization scheme based on the celebrated Stein's…