4 papers · 1 filter
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…
A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
Adrien Weihs, Jingmin Sun, Zecheng Zhang +1
While many problems in machine learning focus on learning mappings between finite-dimensional spaces, scientific applications require approximating mappings between function spaces…
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…
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…