31 citations · 70 across the 8 of their papers we have counts for
4 papers · 1 filter
SAIL: Self-Augmented Graph Contrastive Learning
Lu Yu, Shichao Pei, Lizhong Ding +4
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirica…
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
Yong Liu, Lizhong Ding, Weiping Wang
Theoretical analysis of the divide-and-conquer based distributed learning with least square loss in the reproducing kernel Hilbert space (RKHS) have recently been explored within t…
Nearly Optimal Clustering Risk Bounds for Kernel K-Means
Yong Liu, Lizhong Ding, Weiping Wang
In this paper, we study the statistical properties of kernel -means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in…
Efficient Cross-Validation for Semi-Supervised Learning
Yong Liu, Jian Li, Guangjun Wu +2
Manifold regularization, such as laplacian regularized least squares (LapRLS) and laplacian support vector machine (LapSVM), has been widely used in semi-supervised learning, and i…