384 citations · 505 across the 25 of their papers we have counts for
6 papers · 1 filter
Tensor Topic Modeling Via HOSVD
Yating Liu, Claire Donnat
By representing documents as mixtures of topics, topic modeling has allowed the successful analysis of datasets across a wide spectrum of applications ranging from ecology to genet…
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…
Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions
Claire Donnat, Elena Tuzhilina
Canonical correlation analysis is a widespread technique for discovering linear relationships between two sets of variables. In high dimensions, however, standard estimates of the…
One-Bit Total Variation Denoising over Networks with Applications to Partially Observed Epidemics
Claire Donnat, Olga Klopp, Nicolas Verzelen
This paper introduces a novel approach for epidemic nowcasting and forecasting over networks using total variation (TV) denoising, a method inspired by classical signal processing…