37 citations · 95 across the 26 of their papers we have counts for
21 papers · 1 filter
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning
Ruyue Liu, Rong Yin, Yong Liu +4
Graph Contrastive Learning (GCL) has emerged as the foremost approach for self-supervised learning on graph-structured data. GCL reduces reliance on labeled data by learning robust…
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized…
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li, Yong Liu, Wei Wang +2
Recent Newton-type federated learning algorithms have demonstrated linear convergence with respect to the communication rounds. However, communicating Hessian matrices is often unf…
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network
Ruyue Liu, Rong Yin, Yong Liu +1
Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for l…
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…
Sharper Utility Bounds for Differentially Private Models
Yilin Kang, Yong Liu, Jian Li +1
In this paper, by introducing Generalized Bernstein condition, we propose the first high probability excess population risk bound for dif…
Stability and Generalization of Differentially Private Minimax Problems
Yilin Kang, Yong Liu, Jian Li +1
In the field of machine learning, many problems can be formulated as the minimax problem, including reinforcement learning, generative adversarial networks, to just name a few. So…
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…
Towards Sharper Utility Bounds for Differentially Private Pairwise Learning
Yilin Kang, Yong Liu, Jian Li +1
Pairwise learning focuses on learning tasks with pairwise loss functions, depends on pairs of training instances, and naturally fits for modeling relationships between pairs of sam…
Neural Architecture Optimization with Graph VAE
Jian Li, Yong Liu, Jiankun Liu +1
Due to their high computational efficiency on a continuous space, gradient optimization methods have shown great potential in the neural architecture search (NAS) domain. The mappi…
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…
Convolutional Spectral Kernel Learning
Jian Li, Yong Liu, Weiping Wang
Recently, non-stationary spectral kernels have drawn much attention, owing to its powerful feature representation ability in revealing long-range correlations and input-dependent c…
Input Perturbation: A New Paradigm between Central and Local Differential Privacy
Yilin Kang, Yong Liu, Ben Niu +3
Traditionally, there are two models on differential privacy: the central model and the local model. The central model focuses on the machine learning model and the local model focu…
Data Heterogeneity Differential Privacy: From Theory to Algorithm
Yilin Kang, Jian Li, Yong Liu +1
Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP). In this paper, we first give sharper exce…
Weighted Distributed Differential Privacy ERM: Convex and Non-convex
Yilin Kang, Yong Liu, Weiping Wang
Distributed machine learning is an approach allowing different parties to learn a model over all data sets without disclosing their own data. In this paper, we propose a weighted d…
Automated Spectral Kernel Learning
Jian Li, Yong Liu, Weiping Wang
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits t…
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms
Jian Li, Yong Liu, Weiping Wang
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e.g. multi-task learning a…
Towards Sharp Analysis for Distributed Learning with Random Features
Jian Li, Yong Liu, Weiping Wang
In recent studies, the generalization properties for distributed learning and random features assumed the existence of the target concept over the hypothesis space. However, this s…
Max-Diversity Distributed Learning: Theory and Algorithms
Yong Liu, Jian Li, Weiping Wang
We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of t…