Borgwardt, Karsten

40 publications

TMLR 2026 Fast Graph Generation via Autoregressive Noisy Filtration Modeling Markus Krimmel, Jenna Wiens, Karsten Borgwardt, Dexiong Chen

ICLR 2026 Gelato: Graph Edit Distance via Autoregressive Neural Combinatorial Optimization Paolo Pellizzoni, Till Hendrik Schulz, Karsten Borgwardt

ICLR 2026 PolyGraph Discrepancy: A Classifier-Based Metric for Graph Generation Markus Krimmel, Philip Hartout, Karsten Borgwardt, Dexiong Chen

ICLR 2026 Refine Drugs, Don’t Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery Benno Kaech, Luis Wyss, Karsten Borgwardt, Gianvito Grasso

ICLRW 2025 A Comprehensive Library for RNA Structure-Function Modeling Luis Wyss, Vincent Mallet, Wissam Karroucha, Karsten Borgwardt, Carlos Oliver

NeurIPS 2025 Flatten Graphs as Sequences: Transformers Are Scalable Graph Generators Dexiong Chen, Markus Krimmel, Karsten Borgwardt

ICLR 2025 Graph Neural Networks Can (Often) Count Substructures Paolo Pellizzoni, Till Hendrik Schulz, Karsten Borgwardt

ICLR 2025 Learning Long Range Dependencies on Graphs via Random Walks Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt

NeurIPS 2024 On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks Paolo Pellizzoni, Till Hendrik Schulz, Dexiong Chen, Karsten Borgwardt

ICMLW 2024 Structure- and Function-Aware Substitution Matrices via Differentiable Graph Matching Paolo Pellizzoni, Carlos Oliver, Karsten Borgwardt

ICML 2023 Fisher Information Embedding for Node and Graph Learning Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt

NeurIPS 2023 ProteinShake: Building Datasets and Benchmarks for Deep Learning on Protein Structures Tim Kucera, Carlos Oliver, Dexiong Chen, Karsten Borgwardt

ICLR 2023 Unsupervised Manifold Alignment with Joint Multidimensional Scaling Dexiong Chen, Bowen Fan, Carlos Oliver, Karsten Borgwardt

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

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

ICML 2022 Structure-Aware Transformer for Graph Representation Learning Dexiong Chen, Leslie O’Bray, Karsten Borgwardt

ICLR 2022 Topological Graph Neural Networks Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt

NeurIPSW 2020 Challenging Euclidean Topological Autoencoders Michael Moor, Max Horn, Karsten Borgwardt, 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

NeurIPS 2016 Finding Significant Combinations of Features in the Presence of Categorical Covariates Laetitia Papaxanthos, Felipe Llinares-López, Dean Bodenham, Karsten Borgwardt

NeurIPS 2015 Halting in Random Walk Kernels Mahito Sugiyama, Karsten Borgwardt

NeurIPS 2013 It Is All in the Noise: Efficient Multi-Task Gaussian Process Inference with Structured Residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle

NeurIPS 2013 Rapid Distance-Based Outlier Detection via Sampling Mahito Sugiyama, Karsten Borgwardt

NeurIPS 2013 Scalable Kernels for Graphs with Continuous Attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

JMLR 2012 Feature Selection via Dependence Maximization Le Song, Alex Smola, Arthur Gretton, Justin Bedo, Karsten Borgwardt

NeurIPS 2011 Efficient Inference in Matrix-Variate Gaussian Models with \iid Observation Noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten Borgwardt

AISTATS 2009 A Kernel Method for Unsupervised Structured Network Inference Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten Borgwardt

AISTATS 2009 Efficient Graphlet Kernels for Large Graph Comparison Nino Shervashidze, Svn Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten Borgwardt

NeurIPS 2009 Fast Subtree Kernels on Graphs Nino Shervashidze, Karsten Borgwardt

NeurIPS 2007 Colored Maximum Variance Unfolding Le Song, Arthur Gretton, Karsten Borgwardt, Alex J. Smola

NeurIPS 2006 A Kernel Method for the Two-Sample-Problem Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, Alex J. Smola

NeurIPS 2006 Correcting Sample Selection Bias by Unlabeled Data Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, Alex J. Smola

NeurIPS 2006 Fast Computation of Graph Kernels Karsten Borgwardt, Nicol N. Schraudolph, S.v.n. Vishwanathan