Journal Articles
Sparse Quadratic Approximation for Graph Learning
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
D. Pasadakis, M. Bollhöfer and O. Schenk, "Sparse Quadratic Approximation for Graph Learning," in IEEE Transactions on Pattern Analysis and Machine Intelligence. https://ieeexplore.ieee.org/document/10091452
Multiway p-spectral graph cuts on Grassmann manifolds
Published in Machine Learning, 2022
D. Pasadakis, C.L. Alappat, O. Schenk and G. Wellein, "Multiway p-spectral graph cuts on Grassmann manifolds," in Machine Learning 111, 791–829 (2022). https://link.springer.com/article/10.1007/s10994-021-06108-1
Block-enhanced precision matrix estimation for large-scale datasets
Published in Journal of Computational Science, 2021
A. Eftekhari, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk, Block-enhanced precision matrix estimation for large-scale datasets, Journal of Computational Science, Volume 53, 2021, 101389, ISSN 1877-7503 https://doi.org/10.1016/j.jocs.2021.101389
Conference Papers
Balanced Graph Partition Refinement using the Graph p-Laplacian
Published in PASC' 18: Proceedings of the Platform for Advanced Scientific Computing Conference., 2018
Simpson T, Pasadakis D, Kourounis D, Fujita K, Yamaguchi T, Ichimura T, Schenk O (2018) Balanced graph partition refinement using the graph p-laplacian. In: Proceedings of the Platform for Advanced Scientific Computing Conference, Association for Computing Machinery, New York, NY, USA, PASC ’18 https://doi.org/10.1145/3218176.3218232
Under Review
Large-Scale Precision Matrix Estimation With SQUIC
Published in SSRN, 2021
A. Eftekhari, L. Gaedke-Merzhäuser, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk, "Large-Scale Precision Matrix Estimation With SQUIC" (August 12, 2021). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3904001