Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures. - Bastian Rieck; Hubert Mara; Heike Leitte (IEEE Trans. Vis. Comput. Graph., 2012)

Structural Analysis of Multivariate Point Clouds Using Simplicial Chains. - Bastian Rieck; Heike Leitte (Comput. Graph. Forum, 2014)

Persistent Homology for the Evaluation of Dimensionality Reduction Schemes. - Bastian Rieck; Heike Leitte (Comput. Graph. Forum, 2015)

Interactive Similarity Analysis and Error Detection in Large Tree Collections. - Jens Fangerau; Burkhard Höckendorf; Bastian Rieck; Christian Heine 002; Joachim Wittbrodt; Heike Leitte (Visualization in Medicine and Life Sciences III, 2016)

Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology. - Bastian Rieck; Heike Leitte (Comput. Graph. Forum, 2016)

Persistent homology in multivariate data visualization. - Bastian Rieck (2017)

Visualization of Fullerene Fragmentation. - Kai Sdeo; Bastian Rieck; Filip Sadlo (PacificVis, 2018)

Visualization of Parameter Sensitivity of 2D Time-Dependent Flow. - Karsten Hanser; Ole Klein; Bastian Rieck; Bettina Wiebe; Tobias Selz; Marian Piatkowski; Antoni Sagristà; Boyan Zheng; Mária Lukácová-Medvid'ová; George Craig; Heike Leitte; Filip Sadlo (ISVC, 2018)

Association mapping in biomedical time series via statistically significant shapelet mining. - Christian Bock; Thomas Gumbsch; Michael Moor; Bastian Rieck; Damian Roqueiro; Karsten M. Borgwardt (Bioinform., 2018)

Visualization of 4D Vector Field Topology. - Lutz Hofmann; Bastian Rieck; Filip Sadlo (Comput. Graph. Forum, 2018)

Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks. - Bastian Rieck; Ulderico Fugacci; Jonas Lukasczyk; Heike Leitte (IEEE Trans. Vis. Comput. Graph., 2018)

A Wasserstein Subsequence Kernel for Time Series. - Christian Bock; Matteo Togninalli; M. Elisabetta Ghisu; Thomas Gumbsch; Bastian Rieck; Karsten M. Borgwardt (ICDM, 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 M. Borgwardt (ICLR (Poster), 2019)

A Persistent Weisfeiler-Lehman Procedure for Graph Classification. - Bastian Rieck; Christian Bock; Karsten M. Borgwardt (ICML, 2019)

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. - Michael Moor; Max Horn; Bastian Rieck; Damian Roqueiro; Karsten M. Borgwardt (MLHC, 2019)

Wasserstein Weisfeiler-Lehman Graph Kernels. - Matteo Togninalli; M. Elisabetta Ghisu; Felipe Llinares-López; Bastian Rieck; Karsten M. Borgwardt (NeurIPS, 2019)

Visualization of Equivalence in 2D Bivariate Fields. - Boyan Zheng; Bastian Rieck; Heike Leitte; Filip Sadlo (Comput. Graph. Forum, 2019)

Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis. - Michael Moor; Max Horn; Bastian Rieck; Damian Roqueiro; Karsten M. Borgwardt (CoRR, 2019)

Machine learning for early prediction of circulatory failure in the intensive care unit. - Stephanie L. Hyland; Martin Faltys; Matthias Hüser; Xinrui Lyu; Thomas Gumbsch; Cristóbal Esteban; Christian Bock; Max Horn; Michael Moor; Bastian Rieck; Marc Zimmermann; Dean A. Bodenham; et al. (CoRR, 2019)

Persistent Intersection Homology for the Analysis of Discrete Data. - Bastian Rieck; Markus Banagl; Filip Sadlo; Heike Leitte (CoRR, 2019)

Topological Machine Learning with Persistence Indicator Functions. - Bastian Rieck; Filip Sadlo; Heike Leitte (CoRR, 2019)

Graph Filtration Learning. - Christoph D. Hofer; Florian Graf; Bastian Rieck; Marc Niethammer; Roland Kwitt (ICML, 2020)

Set Functions for Time Series. - Max Horn; Michael Moor; Christian Bock; Bastian Rieck; Karsten M. Borgwardt (ICML, 2020)

Topological Autoencoders. - Michael Moor; Max Horn; Bastian Rieck; Karsten M. Borgwardt (ICML, 2020)

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. - Bastian Rieck; Tristan Yates; Christian Bock; Karsten M. Borgwardt; Guy Wolf; Nicholas B. Turk-Browne; Smita Krishnaswamy (NeurIPS, 2020)

Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. - Thomas Gumbsch; Christian Bock; Michael Moor; Bastian Rieck; Karsten M. Borgwardt (Bioinform., 2020)

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. - Caroline Weis; Max Horn; Bastian Rieck; Aline Cuénod; Adrian Egli; Karsten M. Borgwardt (Bioinform., 2020)

Path Imputation Strategies for Signature Models. - Michael Moor; Max Horn; Christian Bock; Karsten M. Borgwardt; Bastian Rieck (CoRR, 2020)

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis. - Jannis Born; Nina Wiedemann; Gabriel Brändle; Charlotte Buhre; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2020)

Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design. - Sarah C. Brüningk; Felix Hensel; Catherine R. Jutzeler; Bastian Rieck (CoRR, 2020)

Topological Data Analysis of copy number alterations in cancer. - Stefan Groha; Caroline Weis; Alexander Gusev; Bastian Rieck (CoRR, 2020)

Graph Kernels: State-of-the-Art and Future Challenges. - Karsten M. Borgwardt; M. Elisabetta Ghisu; Felipe Llinares-López; Leslie O'Bray; Bastian Rieck (Found. Trends Mach. Learn., 2020)

Filtration Curves for Graph Representation. - Leslie O'Bray; Bastian Rieck; Karsten M. Borgwardt (KDD, 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 (MLHC, 2021)

A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. - Malte Lücken; Daniel Burkhardt; Robrecht Cannoodt; Christopher Lance; Aditi Agrawal; Hananeh Aliee; Ann Chen; Louise Deconinck; Angela Detweiler; Alejandro Granados; Shelly Huynh; Laura Isacco; et al. (NeurIPS Datasets and Benchmarks, 2021)

Network-guided search for genetic heterogeneity between gene pairs. - Anja C. Gumpinger; Bastian Rieck; Dominik G. Grimm; Karsten M. Borgwardt (Bioinform., 2021)

Basic Analysis of Bin-Packing Heuristics. - Bastian Rieck (CoRR, 2021)

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. - Michael Moor; Nicolas Bennett; Drago Plecko; Max Horn; Bastian Rieck; Nicolai Meinshausen; Peter Bühlmann; Karsten M. Borgwardt (CoRR, 2021)

Towards a Taxonomy of Graph Learning Datasets. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2021)

The magnitude vector of images. - Michael F. Adamer; Leslie O'Bray; Edward De Brouwer; Bastian Rieck; Karsten M. Borgwardt (CoRR, 2021)

Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification. - Merel Kuijs; Catherine R. Jutzeler; Bastian Rieck; Sarah C. Brüningk (CoRR, 2021)

A Survey of Topological Machine Learning Methods. - Felix Hensel; Michael Moor; Bastian Rieck (Frontiers Artif. Intell., 2021)

Stable topological signatures for metric trees through graph approximations. - Robin Vandaele; Bastian Rieck; Yvan Saeys; Tijl De Bie (Pattern Recognit. Lett., 2021)

Topological Graph Neural Networks. - Max Horn; Edward De Brouwer; Michael Moor; Yves Moreau; Bastian Rieck; Karsten M. Borgwardt (ICLR, 2022)

Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. - Leslie O'Bray; Max Horn; Bastian Rieck; Karsten M. Borgwardt (ICLR, 2022)

Exploring the Geometry and Topology of Neural Network Loss Landscapes. - Stefan Horoi; Jessie Huang; Bastian Rieck; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (IDA, 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 J. Hirn; Guy Wolf; Ladislav Rampásek (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 001; Gabriele Corso; Leonardo Cotta; Yanqiao Zhu 001; et al. (LoG, 2022)

Capturing Shape Information with Multi-scale Topological Loss Terms for 3D Reconstruction. - Dominik Jens Elias Waibel; Scott Atwell; Matthias Meier; Carsten Marr; Bastian Rieck (MICCAI (4), 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 (NeurIPS, 2022)

On Measuring Excess Capacity in Neural Networks. - Florian Graf; Sebastian Zeng; Bastian Rieck; Marc Niethammer; Roland Kwitt (NeurIPS, 2022)

On the Surprising Behaviour of extttnode2vec. - Celia Hacker; Bastian Rieck (TAG-ML, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2022)

On the Surprising Behaviour of node2vec. - Celia Hacker; Bastian Rieck (CoRR, 2022)

TOAST: Topological Algorithm for Singularity Tracking. - Julius von Rohrscheidt; Bastian Rieck (CoRR, 2022)

Hyperbard (Dataset). - Corinna Coupette; Jilles Vreeken; Bastian Rieck (Zenodo, 2022)

Hyperbard (Code). - Corinna Coupette; Jilles Vreeken; Bastian Rieck (Zenodo, 2022)

Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images from Single 2D Slices. - Kalyan Varma Nadimpalli; Amit Chattopadhyay; Bastian Rieck (CVPR Workshops, 2023)

Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework. - Corinna Coupette; Sebastian Dalleiger; Bastian Rieck (ICLR, 2023)

Topological Singularity Detection at Multiple Scales. - Julius von Rohrscheidt; Bastian Rieck (ICML, 2023)

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images. - Dominik Jens Elias Waibel; Ernst Röell; Bastian Rieck; Raja Giryes; Carsten Marr (ISBI, 2023)

Who can Submit an Excellent Review for this Manuscript in the Next 30 Days? - Peer Reviewing in the Age of Overload. - Hamed Alhoori; Edward A. Fox; Ingo Frommholz; Haiming Liu 002; Corinna Coupette; Bastian Rieck; Tirthankar Ghosal; Jian Wu 006 (JCDL, 2023)

Curvature Filtrations for Graph Generative Model Evaluation. - Joshua Southern; Jeremy Wayland; Michael M. Bronstein; Bastian Rieck (NeurIPS, 2023)

Metric Space Magnitude and Generalisation in Neural Networks. - Rayna Andreeva; Katharina Limbeck; Bastian Rieck; Rik Sarkar (TAG-ML, 2023)

Preface. - Timothy Doster; Tegan Emerson; Henry Kvinge; Nina Miolane; Mathilde Papillon; Bastian Rieck; Sophia Sanborn (TAG-ML, 2023)

ICML 2023 Topological Deep Learning Challenge: Design and Results. - Mathilde Papillon; Mustafa Hajij; Audun Myers; Florian Frantzen; Ghada Zamzmi; Helen Jenne; Johan Mathe; Josef Hoppe; Michael T. Schaub; Theodore Papamarkou; Aldo Guzmán-Sáenz; Bastian Rieck; et al. (TAG-ML, 2023)

On the Expressivity of Persistent Homology in Graph Learning. - Bastian Rieck (CoRR, 2023)

DONUT - Creation, Development, and Opportunities of a Database. - Barbara Giunti; Janis Lazovskis; Bastian Rieck (CoRR, 2023)

MAGNet: Motif-Agnostic Generation of Molecules from Shapes. - Leon Hetzel; Johanna Sommer; Bastian Rieck; Fabian J. Theis; Stephan Günnemann (CoRR, 2023)

Evaluating the "Learning on Graphs" Conference Experience. - Bastian Rieck; Corinna Coupette (CoRR, 2023)

Topologically-Regularized Multiple Instance Learning for Red Blood Cell Disease Classification. - Salome Kazeminia; Ario Sadafi; Asya Makhro; Anna Bogdanova; Carsten Marr; Bastian Rieck (CoRR, 2023)

Filtration Surfaces for Dynamic Graph Classification. - Franz Srambical; Bastian Rieck (CoRR, 2023)

Metric Space Magnitude for Evaluating Unsupervised Representation Learning. - Katharina Limbeck; Rayna Andreeva; Rik Sarkar; Bastian Rieck (CoRR, 2023)

Weisfeiler and Leman go Machine Learning: The Story so far. - Christopher Morris 001; Yaron Lipman; Haggai Maron; Bastian Rieck; Nils M. Kriege; Martin Grohe; Matthias Fey; Karsten M. Borgwardt (J. Mach. Learn. Res., 2023)

A Note on Cherry-Picking in Meta-Analyses. - Daisuke Yoneoka; Bastian Rieck (Entropy, 2023)

Time-Inhomogeneous Diffusion Geometry and Topology. - Guillaume Huguet; Alexander Tong 001; Bastian Rieck; Jessie Huang; Manik Kuchroo; Matthew J. Hirn; Guy Wolf; Smita Krishnaswamy (SIAM J. Math. Data Sci., 2023)

Simplicial Representation Learning with Neural k-Forms. - Kelly Maggs; Celia Hacker; Bastian Rieck (ICLR, 2024)

Differentiable Euler Characteristic Transforms for Shape Classification. - Ernst Röell; Bastian Rieck (ICLR, 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; et al. (ICML, 2024)

Mapping the Multiverse of Latent Representations. - Jeremy Wayland; Corinna Coupette; Bastian Rieck (ICML, 2024)

On the Expressivity of Persistent Homology in Graph Learning. - Rubén Ballester; Bastian Rieck (LoG, 2024)

CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs. - Davide Buffelli; Farzin Soleymani; Bastian Rieck (LoG, 2024)

Metric Space Magnitude for Evaluating the Diversity of Latent Representations. - Katharina Limbeck; Rayna Andreeva; Rik Sarkar; Bastian Rieck (NeurIPS, 2024)

Position Paper: Challenges and Opportunities in Topological Deep Learning. - Theodore Papamarkou; Tolga Birdal; Michael M. Bronstein; Gunnar E. Carlsson; Justin Curry; Yue Gao; Mustafa Hajij; Roland Kwitt; Pietro Liò; Paolo Di Lorenzo; Vasileios Maroulas; Nina Miolane; et al. (CoRR, 2024)

The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning. - Benjamin Holmgren; Eli Quist; Jordan Schupbach; Brittany Terese Fasy; Bastian Rieck (CoRR, 2024)

Persistent Homology via Ellipsoids. - Sara Kalisnik; Bastian Rieck; Ana Zegarac (CoRR, 2024)

Characterizing Physician Referral Networks with Ricci Curvature. - Jeremy Wayland; Russell J. Funk; Bastian Rieck (CoRR, 2024)

Detecting Spatial Dependence in Transcriptomics Data using Vectorised Persistence Diagrams. - Katharina Limbeck; Bastian Rieck (CoRR, 2024)

Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms. - Julius von Rohrscheidt; Bastian Rieck (CoRR, 2024)

Detecting and Approximating Redundant Computational Blocks in Neural Networks. - Irene Cannistraci; Emanuele Rodolà; Bastian Rieck (CoRR, 2024)

Graph Classification Gaussian Processes via Hodgelet Spectral Features. - Mathieu Alain; So Takao; Xiaowen Dong 001; Bastian Rieck; Emmanuel Noutahi (CoRR, 2024)

Topology meets Machine Learning: An Introduction using the Euler Characteristic Transform. - Bastian Rieck (CoRR, 2024)

Generative Topology for Shape Synthesis. - Ernst Röell; Bastian Rieck (CoRR, 2024)

All the world's a (hyper)graph: A data drama. - Corinna Coupette; Jilles Vreeken; Bastian Rieck (Digit. Scholarsh. Humanit., 2024)

Bayesian Computation Meets Topology. - Julius von Rohrscheidt; Bastian Rieck; Sebastian M. Schmon (Trans. Mach. Learn. Res., 2024)

The magnitude vector of images. - Michael F. Adamer; Edward De Brouwer; Leslie O'Bray; Bastian Rieck (J. Appl. Comput. Topol., 2024)

Principal Curvatures Estimation with Applications to Single Cell Data. - Yanlei Zhang; Lydia Mezrag; Xingzhi Sun 003; Charles Xu 004; Kincaid MacDonald; Dhananjay Bhaskar; Smita Krishnaswamy; Guy Wolf; Bastian Rieck (ICASSP, 2025)

MANTRA: The Manifold Triangulations Assemblage. - Rubén Ballester; Ernst Röell; Daniel Bin Schmid; Mathieu Alain; Sergio Escalera; Carles Casacuberta; Bastian Rieck (ICLR, 2025)

MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds. - Leon Hetzel; Johanna Sommer; Bastian Rieck; Fabian J. Theis; Stephan Günnemann (ICLR, 2025)

No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets. - Corinna Coupette; Jeremy Wayland; Emily Simons; Bastian Rieck (CoRR, 2025)

Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations. - Mathieu Alain; So Takao; Xiaowen Dong 001; Bastian Rieck; Emmanuel Noutahi (CoRR, 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 (CoRR, 2025)

Geometry-Aware Edge Pooling for Graph Neural Networks. - Katharina Limbeck; Lydia Mezrag; Guy Wolf; Bastian Rieck (CoRR, 2025)

Molecular Machine Learning Using Euler Characteristic Transforms. - Víctor Toscano-Durán; Florian Rottach; Bastian Rieck (CoRR, 2025)