Frantzen, F., & Schaub, M. T. (2025). HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’25), 2. https://doi.org/10.1145/3711896.3736998
Hoppe, J., Grande, V. P., & Schaub, M. T. (2025). Don’t be Afraid of Cell Complexes! An Introduction from an Applied Perspective. https://arxiv.org/abs/2506.09726
Rompelberg, L., & Schaub, M. T. (2025). A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/ICASSP49660.2025.10889783
Savostianov, A., Schaub, M. T., Guglielmi, N., & Tudisco, F. (2025). Efficient Sparsification of Simplicial Complexes via Local Densities of States. arXiv Preprint arXiv:2502.07558.
Hoppe, J., & Schaub, M. T. (2024). Representing Edge Flows on Graphs via Sparse Cell Complexes. In S. Villar & B. Chamberlain (Eds.), Proceedings of the Second Learning on Graphs Conference (Vol. 231, p. 1:1-1:22). PMLR. https://proceedings.mlr.press/v231/hoppe24a.html
Epping, B., René, A., Helias, M., & Schaub, M. T. (2024). Graph Neural Networks Do Not Always Oversmooth. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), Advances in Neural Information Processing Systems (Vol. 37, pp. 48164–48188). Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2024/file/5623c35f3ab5e2c72aeb3abce27dc28f-Paper-Conference.pdf
Frantzen, F., & Schaub, M. T. (2024). Learning From Simplicial Data Based on Random Walks and 1D Convolutions. The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=OsGUnYOzii
Hajij, M., Papillon, M., Frantzen, F., Agerberg, J., AlJabea, I., Ballester, R., Battiloro, C., Bernárdez, G., Birdal, T., Brent, A., Chin, P., Escalera, S., Fiorellino, S., Gardaa, O. H., Gopalakrishnan, G., Govil, D., Hoppe, J., Karri, M. R., Khouja, J., … Miolane, N. (2024). TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. Journal of Machine Learning Research, 25(374), 1–8. http://jmlr.org/papers/v25/24-0110.html
Savostianov, A., Tudisco, F., & Guglielmi, N. (2024). Cholesky-like Preconditioner for Hodge Laplacians via Heavy Collapsible Subcomplex. SIAM Journal on Matrix Analysis and Applications, 45(4), 1827–1849. https://doi.org/10.1137/23M1626396
Roddenberry, T. M., Grande, V. P., Frantzen, F., Schaub, M. T., & Segarra, S. (2023). Signal Processing On Product Spaces. 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. https://doi.org/10.1109/ICASSP49357.2023.10095735
2022
Roddenberry, T. M., Frantzen, F., Schaub, M. T., & Segarra, S. (2022). Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5922–5926. https://doi.org/10.1109/ICASSP43922.2022.9747203
Schaub, M. T., Seby, J.-B., Frantzen, F., Roddenberry, T. M., Zhu, Y., & Segarra, S. (2022). Signal Processing on Simplicial Complexes. In F. Battiston & G. Petri (Eds.), Higher-Order Systems (pp. 301–328). Springer International Publishing. https://doi.org/10.1007/978-3-030-91374-8_12
2021
Frantzen, F., Seby, J.-B., & Schaub, M. T. (2021). Outlier Detection for Trajectories via Flow-embeddings. 55th Asilomar Conference on Signals, Systems, and Computers, 1568–1572. https://doi.org/10.1109/IEEECONF53345.2021.9723128