My talk on Computational Hypergraph Discovery! This framework addresses the challenge of discovering unknown functional relationships and hypergraph structures from partial observations using Gaussian Processes.
The approach introduces a kernel generalization of Row Echelon Form reduction and variance-based analysis to unveil hypergraph structures, with applications to network discovery in gene pathways, chemical systems, and mechanical systems.