21 citations · 108 across the 18 of their papers we have counts for
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stat.ML2024★ 2 cited
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…
stat.ML2024★ 1 cited
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…