English(EN) SelectPFN: Amortized Bayesian Variable Selection for Sparse Nonlinear Regression with Prior-Data Fitted Networks

SelectPFN 推进了稀疏非线性回归的贝叶斯变量选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了 SelectPFN，这是一种新颖的先验数据拟合网络 (PFN)，能够同时对稀疏非线性回归执行贝叶斯变量选择和预测。该新模型可以在单次前向传播中识别哪些输入变量对其预测影响最大，并提供后验包含概率。在各种数据集上，SelectPFN 表现出优于 lasso 和 BART 等现有方法的性能，尤其是在检测仅交互变量方面。 AI

影响这项研究可以提高稀疏非线性回归任务中使用的模型的解释性和效率。

排序理由该集群包含一篇详细介绍稀疏非线性回归新方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Bin Luo · 2026-10-16 04:00

SelectPFN：稀疏非线性回归的摊销贝叶斯变量选择与先验数据拟合网络

arXiv:2610.21950v1 Announce Type: cross Abstract: Tabular foundation models based on prior-data fitted networks (PFNs) predict on a new data set in a single forward pass, but they cannot report which inputs their predictions depend on. We propose SelectPFN, a PFN that performs Ba…

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SelectPFN：稀疏非线性回归的摊销贝叶斯变量选择与先验数据拟合网络

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