3 papers
stat.ML2026
Learning Sparse Compositional Functions with Norm-Constrained Neural Networks
Shuo Huang, Lorenzo Fiorito, Lorenzo Rosasco +1
The ability of deep neural networks to learn hierarchical features is widely regarded as a key mechanism underlying their success in high-dimensional learning. Existing theory part…
cs.LG2026
Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension
Jianfei Li, Shuo Huang, Han Feng +2
Deep neural networks have emerged as powerful tools for learning operators defined over infinite-dimensional function spaces. However, existing theories frequently encounter diffic…
stat.ML2026
Fine-grained Analysis of Non-parametric Estimation for Pairwise Learning
Junyu Zhou, Shuo Huang, Han Feng +2
In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning. Most of the existing work requires the hypothesis space to b…