25 citations · 88 across the 12 of their papers we have counts for
6 papers · 1 filter
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
Yun Zhu, Yaoke Wang, Haizhou Shi +3
Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on th…
MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning
Yun Zhu, Haizhou Shi, Zhenshuo Zhang +1
In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data. This scenario is particularly challenging beca…
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Zhenshuo Zhang, Yun Zhu, Haizhou Shi +1
Albeit having gained significant progress lately, large-scale graph representation learning remains expensive to train and deploy for two main reasons: (i) the repetitive computati…
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
Haizhou Shi, Youcai Zhang, Zijin Shen +4
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection. Existing works either conduct annot…
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
Haizhou Shi, Youcai Zhang, Siliang Tang +4
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to t…
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Haizhou Shi, Dongliang Luo, Siliang Tang +2
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks. BYO…