arXiv:2604.20111v1 Announce Type: cross Abstract: Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretability. However, most existing models are limited to single-level learning under the mean-squared error criterion, whose empirical performance can degrade significantly in the presence of complex noise, su
Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
Xuelin Zhang, Xinyue Liu, Lingjuan Wu, Hong Chen·arXiv stat.ML··1 min read
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