This repository contains code of the numerical experiments of ICML 2025 paper Testing Conditional Mean Independence Using Generative Neural Networks,Linjun Huang, Yi Zhang, Yun Yang and Xiaofeng Shao.
Conditional mean independence (CMI) testing is a fundamental tool for model simplification and assessing variable importance. However, existing test procedures suffer from severe performance deterioration in high dimensional setting. We propose a new test procedure, basing on a novel CMI measure and neural networks, that has strong empirical performance in scenarios with high-dimensional covariates and response variable. Our test can help in improving model efficiency, accuracy, and interpretability for many machine learning applications.
- Repository
Example_A1_A2contains code used in the experiments for the 3. Simulation Results. - Repository
Facial_Expression_Applicationcontains code used in the experiments for the 4.1. Facial expression recognition. - Repository
Facial_Age_Applicationcontains code used in the experiments for the 4.2. Facial age estimation.
The following packages and versions were used in this project:
- Python: 3.11.12
- torch: 2.6.0+cu124
- numpy: 2.0.2
- scipy: 1.15.2
- xgboost: 2.1.4
- sklearn: 1.6.1
- tqdm: 4.67.1