Schaub, Michael T

14 publications

AISTATS 2025 Global Ground Metric Learning with Applications to scRNA Data Damin Kühn, Michael T Schaub

ICML 2025 Point-Level Topological Representation Learning on Point Clouds Vincent Peter Grande, Michael T Schaub

ICLR 2025 Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs Michael Scholkemper, Xinyi Wu, Ali Jadbabaie, Michael T Schaub

TMLR 2024 Faster Optimal Univariate Microaggregation Felix I. Stamm, Michael T Schaub

NeurIPS 2024 Graph Neural Networks Do Not Always Oversmooth Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub

ICLR 2024 Learning from Simplicial Data Based on Random Walks and 1d Convolutions Florian Frantzen, Michael T Schaub

ICML 2024 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 Veličković, Bei Wang, Yusu Wang, Guowei Wei, Ghada Zamzmi

MLOSS 2024 TopoX: A Suite of Python Packages for Machine Learning on Topological Domains Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane

NeurIPS 2023 An Optimization-Based Approach to Node Role Discovery in Networks: Approximating Equitable Partitions Michael Scholkemper, Michael T Schaub

ICMLW 2023 Non-Isotropic Persistent Homology Vincent Peter Grande, Michael T Schaub

LoG 2023 Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH Vincent Peter Grande, Michael T Schaub

LoG 2023 On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T Schaub, Danai Koutra

LoG 2023 Representing Edge Flows on Graphs via Sparse Cell Complexes Josef Hoppe, Michael T Schaub

ICML 2023 Topological Point Cloud Clustering Vincent Peter Grande, Michael T Schaub