9 citations · 35 across the 11 of their papers we have counts for
11 papers
Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models
Junchi Yu, Ran He, Rex Ying
Large Language Models (LLMs) have achieved remarkable success in reasoning tasks with the development of prompting methods. However, existing prompting approaches cannot reuse insi…
Rumor Detection with Diverse Counterfactual Evidence
Kaiwei Zhang, Junchi Yu, Haichao Shi +2
The growth in social media has exacerbated the threat of fake news to individuals and communities. This draws increasing attention to developing efficient and timely rumor detectio…
Mind the Label Shift of Augmentation-based Graph OOD Generalization
Junchi Yu, Jian Liang, Ran He
Out-of-distribution (OOD) generalization is an important issue for Graph Neural Networks (GNNs). Recent works employ different graph editions to generate augmented environments and…
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data
Jie Cao, Mandi Luo, Junchi Yu +2
Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. To facilitate GAN training, current methods propose to use data-sp…
Finding Diverse and Predictable Subgraphs for Graph Domain Generalization
Junchi Yu, Jian Liang, Ran He
This paper focuses on out-of-distribution generalization on graphs where performance drops due to the unseen distribution shift. Previous graph domain generalization works always r…
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows
Junchi Yu, Tingyang Xu, Ran He
As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparen…
Improving Subgraph Recognition with Variational Graph Information Bottleneck
Junchi Yu, Jie Cao, Ran He
Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property. It can be formulated by optimizing Graph Information B…
Recognizing Predictive Substructures with Subgraph Information Bottleneck
Junchi Yu, Tingyang Xu, Yu Rong +3
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning. However, two disturbing factors, noise and redundancy in graph data, and lack…
Graph Information Bottleneck for Subgraph Recognition
Junchi Yu, Tingyang Xu, Yu Rong +3
Given the input graph and its label/property, several key problems of graph learning, such as finding interpretable subgraphs, graph denoising and graph compression, can be attribu…
Cosmetic-Aware Makeup Cleanser
Yi Li, Huaibo Huang, Junchi Yu +2
Face verification aims at determining whether a pair of face images belongs to the same identity. Recent studies have revealed the negative impact of facial makeup on the verificat…