100 citations · 179 across the 11 of their papers we have counts for
7 papers · 1 filter
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
Meng Liu, Ke Liang, Yawei Zhao +5
Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast…
Multiple Kernel Clustering with Dual Noise Minimization
Junpu Zhang, Liang Li, Siwei Wang +4
Clustering is a representative unsupervised method widely applied in multi-modal and multi-view scenarios. Multiple kernel clustering (MKC) aims to group data by integrating comple…
Simple Contrastive Graph Clustering
Yue Liu, Xihong Yang, Sihang Zhou +1
Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming g…
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Shengyuan Hu, Jack Goetz, Kshitiz Malik +3
Model compression is important in federated learning (FL) with large models to reduce communication cost. Prior works have been focusing on sparsification based compression that co…
Stable Prediction via Leveraging Seed Variable
Kun Kuang, Bo Li, Peng Cui +4
In this paper, we focus on the problem of stable prediction across unknown test data, where the test distribution is agnostic and might be totally different from the training one.…
Risk Variance Penalization
Chuanlong Xie, Haotian Ye, Fei Chen +3
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains. The variance risk extrapolation (V-REx) is a practical…
Causal Discovery by Kernel Intrinsic Invariance Measure
Zhitang Chen, Shengyu Zhu, Yue Liu +1
Reasoning based on causality, instead of association has been considered as a key ingredient towards real machine intelligence. However, it is a challenging task to infer causal re…