103 citations · 430 across the 38 of their papers we have counts for
39 papers
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs
Yichuan Li, Kaize Ding, Kyumin Lee
Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received incr…
Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
Zhangsihao Yang, Mengwei Ren, Kaize Ding +2
Pretraining CNN models (i.e., UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes. Recent contrastiv…
UPREVE: An End-to-End Causal Discovery Benchmarking System
Suraj Jyothi Unni, Paras Sheth, Kaize Ding +2
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making. We present Upload, PREprocess, Visualize, and Evalua…
Federated Few-shot Learning
Song Wang, Xingbo Fu, Kaize Ding +3
Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the…
Virtual Node Tuning for Few-shot Node Classification
Zhen Tan, Ruocheng Guo, Kaize Ding +1
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-…
Uncertainty-Aware Robust Learning on Noisy Graphs
Shuyi Chen, Kaize Ding, Shixiang Zhu
Graph neural networks (GNNs) have excelled in various graph learning tasks, particularly node classification. However, their performance is often hampered by noisy measurements in…