| 2025 | AISTATS | Global Ground Metric Learning with Applications to scRNA data. | Damin Khn, Michael T. Schaub |
| 2025 | ICASSP | A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals. | Lennard Rompelberg, Michael T. Schaub |
| 2025 | ICLR | Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs. | Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T. Schaub |
| 2025 | ICML | Point-Level Topological Representation Learning on Point Clouds. | Vincent Peter Grande, Michael T. Schaub |
| 2025 | KDD | HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection. | Florian Frantzen, Michael T. Schaub |
| 2025 | SDM | Efficient Sampling of Temporal Networks with Preserved Causality Structure. | Felix I. Stamm, Mehdi Naima, Michael T. Schaub |
| 2024 | ACSSC | Topological Trajectory Classification and Landmark Inference on Simplicial Complexes. | Vincent P. Grande, Josef Hoppe, Florian Frantzen, Michael T. Schaub |
| 2024 | ICASSP | Disentangling the Spectral Properties of the Hodge Laplacian: not all small Eigenvalues are Equal. | Vincent P. Grande, Michael T. Schaub |
| 2024 | ICASSP | Optimal Transport Distances for Directed, Weighted Graphs: A Case Study With Cell-Cell Communication Networks. | James Shiniti Nagai, Ivan G. Costa, Michael T. Schaub |
| 2024 | ICASSP | A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings. | Michael Scholkemper, Damin Khn, Gerion Nabbefeld, Simon Musall, Bjrn Kampa, Michael T. Schaub |
| 2024 | ICLR | Learning From Simplicial Data Based on Random Walks and 1D Convolutions. | Florian Frantzen, Michael T. Schaub |
| 2024 | ICML | Position: Topological Deep Learning is the New Frontier for Relational Learning. | Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi |
| 2023 | ACSSC | Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. | Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, AIdo Guzman-Saenz, Tolga Birdal, Michael T. Schaub |
| 2023 | ICASSP | Signal Processing On Product Spaces. | T. Mitchell Roddenberry, Vincent P. Grande, Florian Frantzen, Michael T. Schaub, Santiago Segarra |
| 2023 | ICML | Topological Point Cloud Clustering. | Vincent Peter Grande, Michael T. Schaub |
| 2023 | WWW | Neighborhood Structure Configuration Models. | Felix I. Stamm, Michael Scholkemper, Michael T. Schaub, Markus Strohmaier |
| 2022 | ACSSC | Higher-order signal processing with the Dirac operator. | Lucille Calmon, Michael T. Schaub, Ginestra Bianconi |
| 2022 | ICASSP | Hodgelets: Localized Spectral Representations of Flows On Simplicial Complexes. | T. Mitchell Roddenberry, Florian Frantzen, Michael T. Schaub, Santiago Segarra |
| 2022 | ICASSP | Signal Processing On Cell Complexes. | T. Mitchell Roddenberry, Michael T. Schaub, Mustafa Hajij |
| 2022 | ICASSP | Blind Extraction of Equitable Partitions from Graph Signals. | Michael Scholkemper, Michael T. Schaub |
| 2022 | KDD | How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. | Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra |
| 2021 | ACSSC | Outlier Detection for Trajectories via Flow-embeddings. | Florian Frantzen, Jean-Baptiste Seby, Michael T. Schaub |
| 2019 | ICASSP | Spectral Partitioning of Time-varying Networks with Unobserved Edges. | Michael T. Schaub, Santiago Segarra, Hoi-To Wai |
| 2019 | KDD | Graph-based Semi-Supervised & Active Learning for Edge Flows. | Junteng Jia, Michael T. Schaub, Santiago Segarra, Austin R. Benson |