GRIP2: A Robust and Powerful Deep Knockoff Method for Feature Selection

• URL: http://arxiv.org/abs/2602.00218v1
• Date: Fri, 30 Jan 2026 16:30:49 GMT
• Title: GRIP2: A Robust and Powerful Deep Knockoff Method for Feature Selection
• Authors: Bob Junyi Zou, Lu Tian,
• Abstract summary: Group Regularization Importance Persistence in 2 Dimensions (GRIP2), integrates first-layer feature activity over a two-dimensional regularization surface.<n>In experiments on synthetic and semi-real data, GRIP2 demonstrates improved robustness to feature correlation and noise level.<n>On real-world HIV drug resistance data, GRIP2 recovers known resistance-associated mutations with power better than established linear baselines.
• Score: 9.889589777434283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying truly predictive covariates while strictly controlling false discoveries remains a fundamental challenge in nonlinear, highly correlated, and low signal-to-noise regimes, where deep learning based feature selection methods are most attractive. We propose Group Regularization Importance Persistence in 2 Dimensions (GRIP2), a deep knockoff feature importance statistic that integrates first-layer feature activity over a two-dimensional regularization surface controlling both sparsity strength and sparsification geometry. To approximate this surface integral in a single training run, we introduce efficient block-stochastic sampling, which aggregates feature activity magnitudes across diverse regularization regimes along the optimization trajectory. The resulting statistics are antisymmetric by construction, ensuring finite-sample FDR control. In extensive experiments on synthetic and semi-real data, GRIP2 demonstrates improved robustness to feature correlation and noise level: in high correlation and low signal-to-noise ratio regimes where standard deep learning based feature selectors may struggle, our method retains high power and stability. Finally, on real-world HIV drug resistance data, GRIP2 recovers known resistance-associated mutations with power better than established linear baselines, confirming its reliability in practice.

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