4 citations · 4 across the 7 of their papers we have counts for
7 papers
Pointwise Generalization in Deep Neural Networks
Shaojie Li, Yunbei Xu
We address the fundamental question of why deep neural networks generalize by establishing a pointwise generalization theory for fully connected networks. This framework resolves l…
Towards Sharper Risk Bounds for Minimax Problems
Bowei Zhu, Shaojie Li, Yong Liu
Minimax problems have achieved success in machine learning such as adversarial training, robust optimization, reinforcement learning. For theoretical analysis, current optimal exce…
Stability and Sharper Risk Bounds with Convergence Rate
Bowei Zhu, Shaojie Li, Mingyang Yi +1
Prior work (Klochkov Zhivotovskiy, 2021) establishes at most excess risk bounds via algorithmic stability for strongly-convex learners with high pro…
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping
Shaojie Li, Yong Liu
Gradient clipping is a commonly used technique to stabilize the training process of neural networks. A growing body of studies has shown that gradient clipping is a promising techn…
Understanding the Generalization Performance of Spectral Clustering Algorithms
Shaojie Li, Sheng Ouyang, Yong Liu
The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance. In this paper, we study…
Learning Rates for Nonconvex Pairwise Learning
Shaojie Li, Yong Liu
Pairwise learning is receiving increasing attention since it covers many important machine learning tasks, e.g., metric learning, AUC maximization, and ranking. Investigating the g…
Improved Learning Rates for Stochastic Optimization
Shaojie Li, Pengwei Tang, Yong Liu
Stochastic optimization is a cornerstone of modern machine learning. This paper studies the generalization performance of two classical stochastic optimization algorithms: stochast…