2 papers
stat.ML2026
Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP
Jiaan Han, Junxiao Chen, Yanzhe Fu
Most FDR-controlled feature selection methods are designed for coordinate-wise hypotheses, where each feature has a single weight or importance score. This abstraction fails in seq…
stat.ML2026
CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
Jiaan Han, Junxiao Chen, Yanzhe Fu
We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM. CatNet employs the derivative of SHAP values to qua…