96 citations · 99 across the 19 of their papers we have counts for
8 papers · 1 filter
Filtering with Confidence: When Data Augmentation Meets Conformal Prediction
Zixuan Wu, So Won Jeong, Yating Liu +2
With promising empirical performance across a wide range of applications, synthetic data augmentation appears a viable solution to data scarcity and the demands of increasingly dat…
Graph Topic Modeling for Documents with Spatial or Covariate Dependencies
Yeo Jin Jung, Claire Donnat
We address the challenge of incorporating document-level metadata into topic modeling to improve topic mixture estimation. To overcome the computational complexity and lack of theo…
Understanding the Effect of GCN Convolutions in Regression Tasks
Juntong Chen, Johannes Schmidt-Hieber, Claire Donnat +1
Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. Despite their widespread success across various application…
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks
Jihee You, So Won Jeong, Claire Donnat
With the proliferation of Graph Neural Network (GNN) methods stemming from contrastive learning, unsupervised node representation learning for graph data is rapidly gaining tractio…
A Simplified Framework for Contrastive Learning for Node Representations
Ilgee Hong, Huy Tran, Claire Donnat
Contrastive learning has recently established itself as a powerful self-supervised learning framework for extracting rich and versatile data representations. Broadly speaking, cont…
Tuning the Geometry of Graph Neural Networks
Sowon Jeong, Claire Donnat
By recursively summing node features over entire neighborhoods, spatial graph convolution operators have been heralded as key to the success of Graph Neural Networks (GNNs). Yet, d…