5 papers
Minimax-Optimal Generalization Bounds for Smooth Deep Neural Networks Trained by (Stochastic) Gradient Descent
Junyu Zhou, Puyu Wang, Yunwen Lei +3
Characterizing the optimization dynamics and statistical performance of over-parameterized deep neural networks (DNNs) remains a central challenge in understanding the remarkable s…
Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks
Junyu Zhou, Puyu Wang, Yunwen Lei +2
Recent progress has been made in understanding the statistical generalization performance of gradient descent methods for overparameterized neural networks within the neural tangen…
Generalization analysis with deep ReLU networks for metric and similarity learning
Junyu Zhou, Puyu Wang, Ding-Xuan Zhou
While metric and similarity learning has been extensively studied from several theoretical perspectives, a rigorous understanding of its generalization performance is still lacking…
Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
Puyu Wang, Junyu Zhou, Philipp Liznerski +1
Kolmogorov--Arnold Networks (KANs) have recently emerged as a structured alternative to standard MLPs, yet a principled theory for their training dynamics, generalization, and priv…
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…