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…