3 papers
cs.LG2025
Grassmanian Interpolation of Low-Pass Graph Filters: Theory and Applications
Anton Savostianov, Michael T. Schaub, Benjamin Stamm
Low-pass graph filters are fundamental for signal processing on graphs and other non-Euclidean domains. However, the computation of such filters for parametric graph families can b…
stat.ML2025
Efficient Sparsification of Simplicial Complexes via Local Densities of States
Anton Savostianov, Michael T. Schaub, Nicola Guglielmi +1
Simplicial complexes (SCs) have become a popular abstraction for analyzing complex data using tools from topological data analysis or topological signal processing. However, the an…
cs.LG2025
Convergence of gradient based training for linear Graph Neural Networks
Dhiraj Patel, Anton Savostianov, Michael T. Schaub
Graph Neural Networks (GNNs) are powerful tools for addressing learning problems on graph structures, with a wide range of applications in molecular biology and social networks. Ho…