86 citations · 255 across the 20 of their papers we have counts for
7 papers · 1 filter
Spectral Augmentation for Self-Supervised Learning on Graphs
Lu Lin, Jinghui Chen, Hongning Wang
Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily re…
How Powerful is Implicit Denoising in Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +3
Graph Neural Networks (GNNs), which aggregate features from neighbors, are widely used for graph-structured data processing due to their powerful representation learning capabiliti…
One-shot Neural Backdoor Erasing via Adversarial Weight Masking
Shuwen Chai, Jinghui Chen
Recent studies show that despite achieving high accuracy on a number of real-world applications, deep neural networks (DNNs) can be backdoored: by injecting triggered data samples…
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model
Hangzhi Guo, Feiran Jia, Jinghui Chen +2
Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse (or contrastive)…
Communication-Efficient Adaptive Federated Learning
Yujia Wang, Lu Lin, Jinghui Chen
Federated learning is a machine learning training paradigm that enables clients to jointly train models without sharing their own localized data. However, the implementation of fed…
Do Language Models Plagiarize?
Jooyoung Lee, Thai Le, Jinghui Chen +1
Past literature has illustrated that language models (LMs) often memorize parts of training instances and reproduce them in natural language generation (NLG) processes. However, it…