21 citations · 108 across the 18 of their papers we have counts for
18 papers
Human-Calibrated Automated Testing and Validation of Generative Language Models
Agus Sudjianto, Aijun Zhang, Srinivas Neppalli +2
This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs), with a focus on Retrieval-Augmented Generation (RAG) systems…
Model Validation Practice in Banking: A Structured Approach for Predictive Models
Agus Sudjianto, Aijun Zhang
This paper presents a comprehensive overview of model validation practices and advancement in the banking industry based on the experience of managing Model Risk Management (MRM) s…
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…
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification
Andrew Pangia, Agus Sudjianto, Aijun Zhang +1
Fair lending practices and model interpretability are crucial concerns in the financial industry, especially given the increasing use of complex machine learning models. In respons…
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…
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…
Model-free Subsampling Method Based on Uniform Designs
Mei Zhang, Yongdao Zhou, Zheng Zhou +1
Subsampling or subdata selection is a useful approach in large-scale statistical learning. Most existing studies focus on model-based subsampling methods which significantly depend…
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…
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…
Balance-Subsampled Stable Prediction
Kun Kuang, Hengtao Zhang, Fei Wu +2
In machine learning, it is commonly assumed that training and test data share the same population distribution. However, this assumption is often violated in practice because the s…
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions
Zebin Yang, Aijun Zhang, Agus Sudjianto
The lack of interpretability is an inevitable problem when using neural network models in real applications. In this paper, an explainable neural network based on generalized addit…
Enhancing Explainability of Neural Networks through Architecture Constraints
Zebin Yang, Aijun Zhang, Agus Sudjianto
Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. The neural networks are…
Interval-valued Data Prediction via Regularized Artificial Neural Network
Zebin Yang, Dennis K. J. Lin, Aijun Zhang
A regularized artificial neural network (RANN) is proposed for interval-valued data prediction. The ANN model is selected due to its powerful capability in fitting linear and nonli…