ML Anthology

Rieck, Bastian

48 publications

TMLR 2026 Topological Inductive Bias Fosters Multiple Instance Learning in Data-Scarce Scenarios Salome Kazeminia, Carsten Marr, Bastian Rieck
LoG 2025 CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs Davide Buffelli, Farzin Soleymani, Bastian Rieck
ICML 2025 Diss-L-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms Julius von Rohrscheidt, Bastian Rieck
NeurIPS 2025 Geometry-Aware Edge Pooling for Graph Neural Networks Katharina Limbeck, Lydia Mezrag, Guy Wolf, Bastian Rieck
ICLRW 2025 Graph Networks Struggle with Variable Scale Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers
NeurIPS 2025 Less Is More: Local Intrinsic Dimensions of Contextual Language Models Benjamin Matthias Ruppik, Julius von Rohrscheidt, Carel van Niekerk, Michael Heck, Renato Vukovic, Shutong Feng, Hsien-chin Lin, Nurul Lubis, Bastian Rieck, Marcus Zibrowius, Milica Gasic
ICLR 2025 MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J Theis, Stephan Günnemann
ICLR 2025 MANTRA: The Manifold Triangulations Assemblage Rubén Ballester, Ernst Röell, Daniel Bin Schmid, Mathieu Alain, Sergio Escalera, Carles Casacuberta, Bastian Rieck
ICML 2025 No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets Corinna Coupette, Jeremy Wayland, Emily Simons, Bastian Rieck
ICLRW 2025 On Incorporating Scale into Graph Networks Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers
ICLRW 2025 On Multi-Scale Graph Representation Learning Christian Koke, Dominik Schnaus, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers
LoG 2025 On the Expressivity of Persistent Homology in Graph Learning Rubén Ballester, Bastian Rieck
NeurIPS 2025 Point Cloud Synthesis Using Inner Product Transforms Ernst Röell, Bastian Rieck
TMLR 2024 Bayesian Computation Meets Topology Julius von Rohrscheidt, Bastian Rieck, Sebastian M Schmon
ICLR 2024 Differentiable Euler Characteristic Transforms for Shape Classification Ernst Röell, Bastian Rieck
NeurIPSW 2024 Graph Classification Gaussian Processes via Hodgelet Spectral Features Mathieu Alain, So Takao, Bastian Rieck, Xiaowen Dong, Emmanuel Noutahi
ICML 2024 Mapping the Multiverse of Latent Representations Jeremy Wayland, Corinna Coupette, Bastian Rieck
NeurIPS 2024 Metric Space Magnitude for Evaluating the Diversity of Latent Representations Katharina Limbeck, Rayna Andreeva, Rik Sarkar, Bastian Rieck
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
ICLR 2024 Simplicial Representation Learning with Neural $k$-Forms Kelly Maggs, Celia Hacker, Bastian Rieck
NeurIPS 2023 Curvature Filtrations for Graph Generative Model Evaluation Joshua Southern, Jeremy Wayland, Michael Bronstein, Bastian Rieck
CVPRW 2023 Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images from Single 2D Slices Kalyan Varma Nadimpalli, Amit Chattopadhyay, Bastian Rieck
ICMLW 2023 Metric Space Magnitude and Generalisation in Neural Networks Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar
ICLR 2023 Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework Corinna Coupette, Sebastian Dalleiger, Bastian Rieck
ICMLW 2023 On the Expressive Power of Ollivier-Ricci Curvature on Graphs Joshua Southern, Jeremy Wayland, Michael M. Bronstein, Bastian Rieck
ICML 2023 Topological Singularity Detection at Multiple Scales Julius von Rohrscheidt, Bastian Rieck
JMLR 2023 Weisfeiler and Leman Go Machine Learning: The Story so Far Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt
NeurIPSW 2022 Approximate Bayesian Computation for Panel Data with Signature Maximum Mean Discrepancies Joel Dyer, John Fitzgerald, Bastian Rieck, Sebastian M Schmon
NeurIPS 2022 Diffusion Curvature for Estimating Local Curvature in High Dimensional Data Dhananjay Bhaskar, Kincaid MacDonald, Oluwadamilola Fasina, Dawson Thomas, Bastian Rieck, Ian Adelstein, Smita Krishnaswamy
ICLRW 2022 Diffusion-Based Methods for Estimating Curvature in Data Dhananjay Bhaskar, Kincaid MacDonald, Dawson Thomas, Sarah Zhao, Kisung You, Jennifer Paige, Yariv Aizenbud, Bastian Rieck, Ian M Adelstein, Smita Krishnaswamy
ICLR 2022 Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt
NeurIPS 2022 On Measuring Excess Capacity in Neural Networks Florian Graf, Sebastian Zeng, Bastian Rieck, Marc Niethammer, Roland Kwitt
LoG 2022 Taxonomy of Benchmarks in Graph Representation Learning Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O’ Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek
LoG 2022 The First Learning on Graphs Conference: Preface Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle Li, Sofia Bourhim, Ilia Igashov
ICLR 2022 Topological Graph Neural Networks Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt
MLHC 2021 Back to the Basics with Inclusion of Clinical Domain Knowledge - A Simple, Scalable and Effective Model of Alzheimer’s Disease Classification Sarah C. Brüningk, Felix Hensel, Louis P. Lukas, Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck
ICLRW 2021 Exploring Epithelial-Cell Calcium Signaling with Geometric and Topological Data Analysis Feng Gao, Jessica Moore, Bastian Rieck, Valentina Greco, Smita Krishnaswamy
NeurIPSW 2020 Challenging Euclidean Topological Autoencoders Michael Moor, Max Horn, Karsten Borgwardt, Bastian Rieck
ICML 2020 Graph Filtration Learning Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt
FnTML 2020 Graph Kernels: State-of-the-Art and Future Challenges Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck
ICMLW 2020 Path Imputation Strategies for Signature Models Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, Bastian Rieck
ICML 2020 Set Functions for Time Series Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt
ICML 2020 Topological Autoencoders Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
NeurIPS 2020 Uncovering the Topology of Time-Varying fMRI Data Using Cubical Persistence Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, Smita Krishnaswamy
ICML 2019 A Persistent Weisfeiler-Lehman Procedure for Graph Classification Bastian Rieck, Christian Bock, Karsten Borgwardt
MLHC 2019 Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt
ICLR 2019 Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt
NeurIPS 2019 Wasserstein Weisfeiler-Lehman Graph Kernels Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt