4 papers
Topology-Aware Active Learning on Graphs
Harris Hardiman-Mostow, Jack Mauro, Adrien Weihs +1
We propose a graph-topological approach to active learning that directly targets the core challenge of exploration versus exploitation under scarce label budgets. To guide explorat…
GLL: A Differentiable Graph Learning Layer for Neural Networks
Jason Brown, Bohan Chen, Harris Hardiman-Mostow +2
Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods f…
Higher-Order Regularization Learning on Hypergraphs
Adrien Weihs, Andrea L. Bertozzi, Matthew Thorpe
Higher-Order Hypergraph Learning (HOHL) was recently introduced as a principled alternative to classical hypergraph regularization, enforcing higher-order smoothness via powers of…
Analysis of Semi-Supervised Learning on Hypergraphs
Adrien Weihs, Andrea L. Bertozzi, Matthew Thorpe
Hypergraphs provide a natural framework for modeling multiway interactions. We analyze a class of variational semi-supervised learning problems posed on random geometric hypergraph…