73 citations · 123 across the 12 of their papers we have counts for
13 papers
Analysis of regularized federated learning
Langming Liu, Dingxuan Zhou
Federated learning is an efficient machine learning tool for dealing with heterogeneous big data and privacy protection. Federated learning methods with regularization can control…
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
Guangrui Yang, Jianfei Li, Ming Li +2
In this paper, we explore the approximation theory of functions defined on graphs. Our study builds upon the approximation results derived from the -functional. We establish a t…
Lifting the Veil: Unlocking the Power of Depth in Q-learning
Shao-Bo Lin, Tao Li, Shaojie Tang +2
With the help of massive data and rich computational resources, deep Q-learning has been widely used in operations research and management science and has contributed to great succ…
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
Di Wang, Xiaotong Liu, Shao-Bo Lin +1
Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for the same purpose. Distributed…
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
Zhi Han, Baichen Liu, Shao-Bo Lin +1
This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in…
Rates of Approximation by ReLU Shallow Neural Networks
Tong Mao, Ding-Xuan Zhou
Neural networks activated by the rectified linear unit (ReLU) play a central role in the recent development of deep learning. The topic of approximating functions from Hölder space…