Derives approximation rates and excess risk bounds for Frobenius norm-constrained DNNs learning sparse compositional functions on DAGs, applicable to multi-index models and binary trees while avoiding the curse of dimensionality.
Position: A theory of deep learning must include compositional sparsity.arXiv preprint arXiv:2507.02550, 2025
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Learning Sparse Compositional Functions with Norm-Constrained Neural Networks
Derives approximation rates and excess risk bounds for Frobenius norm-constrained DNNs learning sparse compositional functions on DAGs, applicable to multi-index models and binary trees while avoiding the curse of dimensionality.