Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks
The deep neural network is a widely used framework in machine learning that has been widely applied in various fields. However, deep neural networks often involve a large number of parameters and inputs, many of which may be irrelevant to the goal or true output. These parameters and input variables not only increase computational complexity, but also contribute to additional computational cost. One solution to this problem is knockoff methods, which have proven successful in controlling false discovery rates in high-dimensional regression. Building on the knockoff methods and using the regularised neural network, this paper proposes three variable screening methods under the condition of...
| Authors | Wenyu Liao, Yiqing Shi, Fang Xie |
|---|---|
| Primary category | stat.ML |
| Published | 3 June 2026 |
| Last updated at source | 17 June 2026 |
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