神经网络机器回归（NNMR）：一种用于揭示高阶协同效应的深度学习框架
Neural Network Machine Regression (NNMR): A Deep Learning Framework for Uncovering High-order Synergistic Effects
AI总结：
NNMR通过整合可训练输入门控和自适应深度正则化，实现端到端的特征选择与函数估计，有效揭示高阶协同效应，提升高维数据下的推断可靠性与选择准确性。
摘要：
我们提出了一种新的神经网络框架，称为神经网络机器回归（NNMR），该框架集成了可训练的输入门控和自适应深度正则化，以端到端的方式联合执行特征选择和函数估计。通过惩罚门控参数和冗余层，NNMR在获得稀疏且可解释的架构的同时，捕捉由高阶协同效应驱动的复杂非线性关系。我们进一步开发了一种基于分样本和排列检验的后选择推断程序，使推断在不依赖限制性参数假设的情况下变得有效。与现有方法相比，包括贝叶斯核机器回归和广泛使用的事后归因技术，NNMR在高维特征空间中能够高效扩展，同时严格控制I型错误。模拟研究展示了其优越的选择准确性和推断可靠性。最后，一个实证应用揭示了与墨西哥城青少年躯体生长相关的稀疏、具有生物学意义的食物组预测因子。
英文摘要：
We propose a new neural network framework, termed Neural Network Machine Regression (NNMR), which integrates trainable input gating and adaptive depth regularization to jointly perform feature selection and function estimation in an end-to-end manner. By penalizing both gating parameters and redundant layers, NNMR yields sparse and interpretable architectures while capturing complex nonlinear relationships driven by high-order synergistic effects. We further develop a post-selection inference procedure based on split-sample, permutation-based hypothesis testing, enabling valid inference without restrictive parametric assumptions. Compared with existing methods, including Bayesian kernel machine regression and widely used post hoc attribution techniques, NNMR scales efficiently to high-dimensional feature spaces while rigorously controlling type I error. Simulation studies demonstrate its superior selection accuracy and inference reliability. Finally, an empirical application reveals sparse, biologically meaningful food group predictors associated with somatic growth among adolescents living in Mexico City.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
