default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKarsten M. Borgwardt
Karsten Michael Borgwardt
Person information
- affiliation: Max Planck Institute of Biochemistry, Munich, Germany
- affiliation (former): ETH Zurich, Department of Biosystems Science and Engineering
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
showing all ?? records
Books and Theses
- 2007
[b1]Karsten Michael Borgwardt:
Graph kernels. Ludwig Maximilians University Munich, Germany, 2007, pp. 1-170
Journal Articles
- 2026
- [j52]Nina Corvelo Benz, Lucas Miranda, Dexiong Chen
, Janko Sattler, Karsten M. Borgwardt:
Conformal Prediction with Knowledge Graphs for Reliable Antimicrobial Resistance Detection with MALDI-TOF Mass Spectra. J. Comput. Biol. 33(1): 19-35 (2026) - [j51]Markus Krimmel, Jenna Wiens, Karsten M. Borgwardt, Dexiong Chen:
Fast Graph Generation via Autoregressive Noisy Filtration Modeling. Trans. Mach. Learn. Res. 2026 (2026) - 2025
- [j50]Philip Hartout, Dexiong Chen, Paolo Pellizzoni, Carlos G. Oliver, Karsten M. Borgwardt:
Endowing protein language models with structural knowledge. Bioinform. 41(11) (2025) - [j49]Karsten M. Borgwardt, Tijana Milenkovic:
ISMB/ECCB 2025 Proceedings. Bioinform. 41(Supplement_1): i1-i2 (2025) - 2024
- [j48]Xinrui Lyu, Bowen Fan, Matthias Hüser, Philip Hartout, Thomas Gumbsch, Martin Faltys, Tobias M. Merz, Gunnar Rätsch, Karsten M. Borgwardt:
An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinform. 40(Supplement_1): i247-i256 (2024) - [j47]Michael F. Adamer, Sarah C. Brüningk, Dexiong Chen, Karsten M. Borgwardt:
Biomarker identification by interpretable maximum mean discrepancy. Bioinform. 40(Supplement_1): i501-i510 (2024) - 2023
- [j46]Michael F. Adamer, Eljas Roellin, Lucie Bourguignon, Karsten M. Borgwardt:
SIMBSIG: similarity search and clustering for biobank-scale data. Bioinform. 39(1) (2023) - [j45]Giulia Muzio, Leslie O'Bray, Laetitia Meng-Papaxanthos, Juliane Klatt, Krista Fischer, Karsten M. Borgwardt:
networkGWAS : a network-based approach to discover genetic associations. Bioinform. 39(6) (2023) - [j44]Matteo Togninalli, Xu Wang, Tim Kucera, Sandesh Shrestha, Philomin Juliana, Suchismita Mondal, Francisco Pinto Espinosa, Velu Govindan, Leonardo Crespo-Herrera, Julio Huerta-Espino, Ravi P. Singh, Karsten M. Borgwardt, Jesse Poland:
Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics. Bioinform. 39(6) (2023) - [j43]Giovanni Visonà, Diane Duroux, Lucas Miranda, Emese Sükei, Yiran Li, Karsten M. Borgwardt, Carlos G. Oliver:
Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information. Bioinform. 39(12) (2023) - [j42]Paolo Pellizzoni, Giulia Muzio, Karsten M. Borgwardt:
Higher-order genetic interaction discovery with network-based biological priors. Bioinform. 39(Supplement-1): 523-533 (2023) - [j41]Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt:
Weisfeiler and Leman go Machine Learning: The Story so far. J. Mach. Learn. Res. 24: 333:1-333:59 (2023) - 2022
- [j40]Bowen Fan, Juliane Klatt, Michael Moor, Latasha A. Daniels, Philipp K. A Agyeman, Christoph Berger, Eric Giannoni, Martin Stocker, Klara M. Posfay-Barbe, Ulrich Heininger, Sara Bernhard-Stirnemann, Anita Niederer-Loher, Christian R. Kahlert, Giancarlo Natalucci, Christa Relly, Thomas Riedel, Christoph Aebi, Luregn J. Schlapbach, Lazaro N. Sanchez-Pinto, Karsten M. Borgwardt:
Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients. Bioinform. 38(Supplement_1): i101-i108 (2022) - 2021
- [j39]Giulia Muzio, Leslie O'Bray, Karsten M. Borgwardt:
Biological network analysis with deep learning. Briefings Bioinform. 22(2): 1515-1530 (2021) - [j38]Anja C. Gumpinger, Bastian Rieck, Dominik G. Grimm, Karsten M. Borgwardt:
Network-guided search for genetic heterogeneity between gene pairs. Bioinform. 37(1): 57-65 (2021) - 2020
- [j37]Caroline Weis, Max Horn, Bastian Rieck, Aline Cuénod, Adrian Egli, Karsten M. Borgwardt:
Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. Bioinform. 36(Supplement-1): i30-i38 (2020) - [j36]Anja C. Gumpinger, Kasper Lage, Heiko Horn, Karsten M. Borgwardt:
Prediction of cancer driver genes through network-based moment propagation of mutation scores. Bioinform. 36(Supplement-1): i508-i515 (2020) - [j35]Thomas Gumbsch, Christian Bock, Michael Moor, Bastian Rieck, Karsten M. Borgwardt:
Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. Bioinform. 36(Supplement-2): i840-i848 (2020) - [j34]Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck:
Graph Kernels: State-of-the-Art and Future Challenges. Found. Trends Mach. Learn. 13(5-6) (2020) - [j33]Xiao He, Thomas Gumbsch, Damian Roqueiro, Karsten M. Borgwardt:
Kernel conditional clustering and kernel conditional semi-supervised learning. Knowl. Inf. Syst. 62(3): 899-925 (2020) - [j32]Matteo Togninalli, Ümit Seren, Jan A. Freudenthal, J. Grey Monroe, Dazhe Meng, Magnus Nordborg, Detlef Weigel, Karsten M. Borgwardt, Arthur Korte, Dominik G. Grimm:
AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana. Nucleic Acids Res. 48(Database-Issue): D1063-D1068 (2020) - 2019
- [j31]Felipe Llinares-López, Laetitia Papaxanthos, Damian Roqueiro, Dean A. Bodenham, Karsten M. Borgwardt:
CASMAP: detection of statistically significant combinations of SNPs in association mapping. Bioinform. 35(15): 2680-2682 (2019) - [j30]Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen:
Introduction to the special issue for the ECML PKDD 2019 journal track. Data Min. Knowl. Discov. 33(5): 1223-1224 (2019) - [j29]Karsten M. Borgwardt, Po-Ling Loh, Evimaria Terzi, Antti Ukkonen:
Introduction to the special issue for the ECML PKDD 2019 journal track. Mach. Learn. 108(8-9): 1191-1192 (2019) - 2018
- [j28]Mahito Sugiyama, M. Elisabetta Ghisu, Felipe Llinares-López, Karsten M. Borgwardt:
graphkernels: R and Python packages for graph comparison. Bioinform. 34(3): 530-532 (2018) - [j27]Christian Bock, Thomas Gumbsch, Michael Moor, Bastian Rieck, Damian Roqueiro, Karsten M. Borgwardt:
Association mapping in biomedical time series via statistically significant shapelet mining. Bioinform. 34(13): i438-i446 (2018) - [j26]Xiao He, Lukas Folkman, Karsten M. Borgwardt:
Kernelized rank learning for personalized drug recommendation. Bioinform. 34(16): 2808-2816 (2018) - [j25]Matteo Togninalli, Damian Roqueiro, COPDGene Investigators, Karsten M. Borgwardt:
Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts. Bioinform. 34(17): i687-i696 (2018) - [j24]Limin Li, Xiao He, Karsten M. Borgwardt:
Multi-target drug repositioning by bipartite block-wise sparse multi-task learning. BMC Syst. Biol. 12(4): 85-97 (2018) - [j23]Matteo Togninalli, Ümit Seren, Dazhe Meng, Joffrey Fitz, Magnus Nordborg, Detlef Weigel, Karsten M. Borgwardt, Arthur Korte, Dominik G. Grimm:
The AraGWAS Catalog: a curated and standardized Arabidopsis thaliana GWAS catalog. Nucleic Acids Res. 46(Database-Issue): D1150-D1156 (2018) - 2017
- [j22]Felipe Llinares-López, Laetitia Papaxanthos, Dean A. Bodenham, Damian Roqueiro, COPDGene Investigators, Karsten M. Borgwardt:
Genome-wide genetic heterogeneity discovery with categorical covariates. Bioinform. 33(12): 1820-1828 (2017) - [j21]Ümit Seren, Dominik G. Grimm, Joffrey Fitz, Detlef Weigel, Magnus Nordborg, Karsten M. Borgwardt, Arthur Korte:
AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic Acids Res. 45(Database-Issue): D1054-D1059 (2017) - 2015
- [j20]Felipe Llinares-López, Dominik G. Grimm, Dean A. Bodenham, Udo Gieraths, Mahito Sugiyama, Beth Rowan, Karsten M. Borgwardt:
Genome-wide detection of intervals of genetic heterogeneity associated with complex traits. Bioinform. 31(12): 240-249 (2015) - [j19]Damian Roqueiro, Menno J. Witteveen, Verneri Anttila, Gisela M. Terwindt, Arn M. J. M. van den Maagdenberg, Karsten M. Borgwardt:
In silico phenotyping via co-training for improved phenotype prediction from genotype. Bioinform. 31(12): 303-310 (2015) - 2013
- [j18]Barbara Rakitsch, Christoph Lippert, Oliver Stegle, Karsten M. Borgwardt:
A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinform. 29(2): 206-214 (2013) - [j17]Nicoló Fusi, Christoph Lippert, Karsten M. Borgwardt, Neil D. Lawrence, Oliver Stegle:
Detecting regulatory gene-environment interactions with unmeasured environmental factors. Bioinform. 29(11): 1382-1389 (2013) - [j16]Chloé-Agathe Azencott, Dominik G. Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt:
Efficient network-guided multi-locus association mapping with graph cuts. Bioinform. 29(13): 171-179 (2013) - 2012
- [j15]Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John M. Winn, Karsten M. Borgwardt:
ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinform. 28(7): 1001-1008 (2012) - [j14]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test. J. Mach. Learn. Res. 13: 723-773 (2012) - [j13]Le Song, Alexander J. Smola, Arthur Gretton, Justin Bedo, Karsten M. Borgwardt:
Feature Selection via Dependence Maximization. J. Mach. Learn. Res. 13: 1393-1434 (2012) - 2011
- [j12]Tony Kam-Thong, Benno Pütz, Nazanin Karbalai, Bertram Müller-Myhsok, Karsten M. Borgwardt:
Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. Bioinform. 27(13): 214-221 (2011) - [j11]Limin Li, Barbara Rakitsch, Karsten M. Borgwardt:
ccSVM: correcting Support Vector Machines for confounding factors in biological data classification. Bioinform. 27(13): 342-348 (2011) - [j10]Karin Klotzbücher, Yasushi Kobayashi, Nino Shervashidze, Oliver Stegle, Bertram Müller-Myhsok, Detlef Weigel, Karsten M. Borgwardt:
Efficient branch-and-bound techniques for two-locus association mapping. BMC Bioinform. 12(S-11): A3 (2011) - [j9]Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt:
Weisfeiler-Lehman Graph Kernels. J. Mach. Learn. Res. 12: 2539-2561 (2011) - [j8]Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan:
Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Mach. Learn. 83(2): 133-135 (2011) - 2010
- [j7]Christoph Lippert, Zoubin Ghahramani, Karsten M. Borgwardt:
Gene function prediction from synthetic lethality networks via ranking on demand. Bioinform. 26(7): 912-918 (2010) - [j6]Oliver Stegle, Katherine J. Denby, Emma J. Cooke, David L. Wild, Zoubin Ghahramani, Karsten M. Borgwardt:
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. J. Comput. Biol. 17(3): 355-367 (2010) - [j5]S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt:
Graph Kernels. J. Mach. Learn. Res. 11: 1201-1242 (2010) - [j4]Gustavo Camps-Valls, Nino Shervashidze, Karsten M. Borgwardt:
Spatio-Spectral Remote Sensing Image Classification With Graph Kernels. IEEE Geosci. Remote. Sens. Lett. 7(4): 741-745 (2010) - [j3]Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt:
Discriminative frequent subgraph mining with optimality guarantees. Stat. Anal. Data Min. 3(5): 302-318 (2010) - 2007
- [j2]Hans-Peter Kriegel, Karsten M. Borgwardt, Peer Kröger, Alexey Pryakhin, Matthias Schubert, Arthur Zimek:
Future trends in data mining. Data Min. Knowl. Discov. 15(1): 87-97 (2007) - 2006
- [j1]S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola:
Kernel extrapolation. Neurocomputing 69(7-9): 721-729 (2006)
Conference and Workshop Papers
- 2025
- [c67]Dexiong Chen, Till Hendrik Schulz, Karsten M. Borgwardt:
Learning Long Range Dependencies on Graphs via Random Walks. ICLR 2025 - [c66]Paolo Pellizzoni, Till Hendrik Schulz, Karsten M. Borgwardt:
Graph Neural Networks Can (Often) Count Substructures. ICLR 2025 - [c65]Nina Corvelo Benz, Lucas Miranda, Dexiong Chen, Janko Sattler, Karsten M. Borgwardt:
Detecting Antimicrobial Resistance Through MALDI-TOF Mass Spectrometry with Statistical Guarantees Using Conformal Prediction. RECOMB 2025: 68-85 - 2024
- [c64]Paolo Pellizzoni, Till Hendrik Schulz, Dexiong Chen, Karsten M. Borgwardt:
On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks. NeurIPS 2024 - [c63]Paolo Pellizzoni, Carlos G. Oliver, Karsten M. Borgwardt:
Structure- and Function-Aware Substitution Matrices via Learnable Graph Matching. RECOMB 2024: 288-307 - 2023
- [c62]Paolo Pellizzoni, Karsten M. Borgwardt:
FASM and FAST-YB: Significant Pattern Mining with False Discovery Rate Control. ICDM 2023: 1265-1270 - [c61]Dexiong Chen, Bowen Fan, Carlos G. Oliver, Karsten M. Borgwardt:
Unsupervised Manifold Alignment with Joint Multidimensional Scaling. ICLR 2023 - [c60]Dexiong Chen, Paolo Pellizzoni, Karsten M. Borgwardt:
Fisher Information Embedding for Node and Graph Learning. ICML 2023: 4839-4855 - [c59]Tim Kucera, Carlos G. Oliver, Dexiong Chen, Karsten M. Borgwardt:
ProteinShake: Building datasets and benchmarks for deep learning on protein structures. NeurIPS 2023 - 2022
- [c58]Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten M. Borgwardt:
Topological Graph Neural Networks. ICLR 2022 - [c57]Leslie O'Bray, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. ICLR 2022 - [c56]Dexiong Chen, Leslie O'Bray, Karsten M. Borgwardt:
Structure-Aware Transformer for Graph Representation Learning. ICML 2022: 3469-3489 - 2021
- [c55]Leslie O'Bray, Bastian Rieck, Karsten M. Borgwardt:
Filtration Curves for Graph Representation. KDD 2021: 1267-1275 - 2020
- [c54]Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten M. Borgwardt:
Set Functions for Time Series. ICML 2020: 4353-4363 - [c53]Michael Moor, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Topological Autoencoders. ICML 2020: 7045-7054 - [c52]Adrian Egli, Manuel Battegay, Andrea C. Büchler, Peter Bühlmann, Thierry Calandra, Philippe Eckert, Hansjakob Furrer, Gilbert Greub, Stephan M. Jakob, Laurent Kaiser, Stephen L. Leib, Stephan Marsch, Nicolai Meinshausen, Jean-Luc Pagani, Jerome Pugin, Gunnar Rätsch, Jacques Schrenzel, Reto Schüpbach, Martin Siegemund, Nicola Zamboni, Reinhard Zbinden, Annelies Zinkernagel, Karsten M. Borgwardt:
SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE 2020: 1163-1167 - [c51]Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. NeurIPS 2020 - 2019
- [c50]Christian Bock, Matteo Togninalli, M. Elisabetta Ghisu, Thomas Gumbsch, Bastian Rieck, Karsten M. Borgwardt:
A Wasserstein Subsequence Kernel for Time Series. ICDM 2019: 964-969 - [c49]Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten M. Borgwardt:
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. ICLR (Poster) 2019 - [c48]Bastian Rieck, Christian Bock, Karsten M. Borgwardt:
A Persistent Weisfeiler-Lehman Procedure for Graph Classification. ICML 2019: 5448-5458 - [c47]Mahito Sugiyama, Karsten M. Borgwardt:
Finding Statistically Significant Interactions between Continuous Features. IJCAI 2019: 3490-3498 - [c46]Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten M. Borgwardt:
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. MLHC 2019: 2-26 - [c45]Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt:
Wasserstein Weisfeiler-Lehman Graph Kernels. NeurIPS 2019: 6436-6446 - 2017
- [c44]Xiao He, Thomas Gumbsch, Damian Roqueiro, Karsten M. Borgwardt:
Kernel Conditional Clustering. ICDM 2017: 157-166 - [c43]Xiao He, Limin Li, Damian Roqueiro, Karsten M. Borgwardt:
Multi-view Spectral Clustering on Conflicting Views. ECML/PKDD (2) 2017: 826-842 - 2016
- [c42]Laetitia Papaxanthos, Felipe Llinares-López, Dean A. Bodenham, Karsten M. Borgwardt:
Finding significant combinations of features in the presence of categorical covariates. NIPS 2016: 2271-2279 - 2015
- [c41]Felipe Llinares-López, Mahito Sugiyama, Laetitia Papaxanthos, Karsten M. Borgwardt:
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing. KDD 2015: 725-734 - [c40]Mahito Sugiyama, Karsten M. Borgwardt:
Halting in Random Walk Kernels. NIPS 2015: 1639-1647 - [c39]Mahito Sugiyama, Felipe Llinares-López, Niklas Kasenburg, Karsten M. Borgwardt:
Significant Subgraph Mining with Multiple Testing Correction. SDM 2015: 37-45 - 2014
- [c38]Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt:
Multi-Task Feature Selection on Multiple Networks via Maximum Flows. SDM 2014: 199-207 - 2013
- [c37]Mahito Sugiyama, Karsten M. Borgwardt:
Measuring Statistical Dependence via the Mutual Information Dimension. IJCAI 2013: 1692-1698 - [c36]Aasa Feragen, Jens Petersen, Dominik G. Grimm, Asger Dirksen, Jesper Johannes Holst Pedersen, Karsten M. Borgwardt, Marleen de Bruijne:
Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry. IPMI 2013: 171-183 - [c35]Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten M. Borgwardt:
Scalable kernels for graphs with continuous attributes. NIPS 2013: 216-224 - [c34]Mahito Sugiyama, Karsten M. Borgwardt:
Rapid Distance-Based Outlier Detection via Sampling. NIPS 2013: 467-475 - [c33]Barbara Rakitsch, Christoph Lippert, Karsten M. Borgwardt, Oliver Stegle:
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. NIPS 2013: 1466-1474 - 2011
- [c32]Panagiotis Achlioptas, Bernhard Schölkopf, Karsten M. Borgwardt:
Two-locus association mapping in subquadratic time. KDD 2011: 726-734 - [c31]Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt:
Efficient inference in matrix-variate Gaussian models with \iid observation noise. NIPS 2011: 630-638 - 2010
- [c30]Bianca Wackersreuther, Peter Wackersreuther, Annahita Oswald, Christian Böhm, Karsten M. Borgwardt:
Frequent subgraph discovery in dynamic networks. MLG@KDD 2010: 155-162 - 2009
- [c29]Oliver Stegle, Katherine J. Denby, David L. Wild, Stuart McHattie, Andrew Meade, Zoubin Ghahramani, Karsten M. Borgwardt:
Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series. GCB 2009: 133-142 - [c28]Risi Kondor, Nino Shervashidze, Karsten M. Borgwardt:
The graphlet spectrum. ICML 2009: 529-536 - [c27]Nino Shervashidze, Karsten M. Borgwardt:
Fast subtree kernels on graphs. NIPS 2009: 1660-1668 - [c26]Oliver Stegle, Katherine J. Denby, David L. Wild, Zoubin Ghahramani, Karsten M. Borgwardt:
A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series. RECOMB 2009: 201-216 - [c25]Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt:
Near-optimal Supervised Feature Selection among Frequent Subgraphs. SDM 2009: 1076-1087 - [c24]Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten M. Borgwardt:
A kernel method for unsupervised structured network inference. AISTATS 2009: 368-375 - [c23]Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt:
Efficient graphlet kernels for large graph comparison. AISTATS 2009: 488-495 - 2008
- [c22]Christian Hübler, Hans-Peter Kriegel, Karsten M. Borgwardt, Zoubin Ghahramani:
Metropolis Algorithms for Representative Subgraph Sampling. ICDM 2008: 283-292 - [c21]Risi Kondor, Karsten M. Borgwardt:
The skew spectrum of graphs. ICML 2008: 496-503 - [c20]Karsten M. Borgwardt, Xifeng Yan, Marisa Thoma, Hong Cheng, Arthur Gretton, Le Song, Alex Smola, Jiawei Han, Philip S. Yu, Hans-Peter Kriegel:
Combining near-optimal feature selection with gSpan. MLG@ICML 2008 - [c19]Christian Hübler, Karsten M. Borgwardt, Hans-Peter Kriegel, Zoubin Ghahramani:
Representative Subgraph Sampling using Markov Chain Monte Carlo Methods. MLG@ICML 2008 - 2007
- [c18]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641 - [c17]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt:
A dependence maximization view of clustering. ICML 2007: 815-822 - [c16]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised feature selection via dependence estimation. ICML 2007: 823-830 - [c15]Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola:
Gene selection via the BAHSIC family of algorithms. ISMB/ECCB (Supplement of Bioinformatics) 2007: 490-498 - [c14]Karsten M. Borgwardt, Tobias Petri, S. V. N. Vishwanathan, Hans-Peter Kriegel:
An Efficient Sampling Scheme For Comparison of Large Graphs. MLG 2007 - [c13]Le Song, Alexander J. Smola, Karsten M. Borgwardt, Arthur Gretton:
Colored Maximum Variance Unfolding. NIPS 2007: 1385-1392 - [c12]Karsten M. Borgwardt, Hans-Peter Kriegel:
Graph Kernels For Disease Outcome Prediction From Protein-Protein Interaction Networks. Pacific Symposium on Biocomputing 2007: 4-15 - 2006
- [c11]Johannes Aßfalg, Karsten M. Borgwardt, Hans-Peter Kriegel:
3DString: a feature string kernel for 3D object classification on voxelized data. CIKM 2006: 198-207 - [c10]Karsten M. Borgwardt, Hans-Peter Kriegel, Peter Wackersreuther:
Pattern Mining in Frequent Dynamic Subgraphs. ICDM 2006: 818-822 - [c9]Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola:
Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57 - [c8]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520 - [c7]Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf:
Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608 - [c6]S. V. N. Vishwanathan, Karsten M. Borgwardt, Nicol N. Schraudolph:
Fast Computation of Graph Kernels. NIPS 2006: 1449-1456 - [c5]Karsten M. Borgwardt, S. V. N. Vishwanathan, Hans-Peter Kriegel:
Class Prediction from Time Series Gene Expression Profiles Using Dynamical Systems Kernels. Pacific Symposium on Biocomputing 2006: 547-558 - [c4]Karsten M. Borgwardt, Sebastian Böttger, Hans-Peter Kriegel:
VGM: visual graph mining. SIGMOD Conference 2006: 733-735 - 2005
- [c3]Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola:
Joint Regularization. ESANN 2005: 455-460 - [c2]Karsten M. Borgwardt, Hans-Peter Kriegel:
Shortest-Path Kernels on Graphs. ICDM 2005: 74-81 - [c1]Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel:
Protein function prediction via graph kernels. ISMB (Supplement of Bioinformatics) 2005: 47-56
Parts in Books or Collections
- 2022
- [p4]Catherine R. Jutzeler, Karsten M. Borgwardt:
Machine Learning in Medicine. Mach. Learn. under Resour. Constraints Vol. 3 (3) 2022: 3-20 - 2021
- [p3]Christian Bock, Michael Moor, Catherine R. Jutzeler, Karsten M. Borgwardt:
Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning. Artificial Neural Networks, 3rd Edition 2021: 33-71 - 2011
- [p2]Karsten M. Borgwardt:
Kernel Methods in Bioinformatics. Handbook of Statistical Bioinformatics 2011: 317-334 - 2007
- [p1]Karsten M. Borgwardt:
Graph-Kerne [Graph Kernels]. Ausgezeichnete Informatikdissertationen 2007: 41-50
Editorship
- 2009
- [e1]Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan:
Proceedings of the Joint 7th Workshop on Mining and Learning with Graphs and the 6th International Workshop on Statistical Relational Learning, MLG/SRL@ILP 2009, Leuven, Belgium, July 2-4, 2009. 2009 [contents]
Informal and Other Publications
- 2026
- [i38]Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten M. Borgwardt:
Protein Fold Classification at Scale: Benchmarking and Pretraining. CoRR abs/2605.18552 (2026) - [i37]Dexiong Chen, Till Hendrik Schulz, Karsten M. Borgwardt:
End-to-End Subgraph Detection with GraphDETR. CoRR abs/2606.06364 (2026) - 2025
- [i36]Dexiong Chen, Markus Krimmel, Karsten M. Borgwardt:
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators. CoRR abs/2502.02216 (2025) - [i35]Markus Krimmel, Jenna Wiens, Karsten M. Borgwardt, Dexiong Chen:
Towards Fast Graph Generation via Autoregressive Noisy Filtration Modeling. CoRR abs/2502.02415 (2025) - [i34]Luis Wyss, Vincent Mallet, Wissam Karroucha, Karsten M. Borgwardt, Carlos G. Oliver:
A Comprehensive Benchmark for RNA 3D Structure-Function Modeling. CoRR abs/2503.21681 (2025) - [i33]Benno Kaech, Luis Wyss, Karsten M. Borgwardt, Gianvito Grasso:
Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery. CoRR abs/2509.26405 (2025) - [i32]Markus Krimmel, Philip Hartout, Karsten M. Borgwardt, Dexiong Chen:
PolyGraph Discrepancy: a classifier-based metric for graph generation. CoRR abs/2510.06122 (2025) - 2024
- [i31]Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos G. Oliver, Karsten M. Borgwardt:
Endowing Protein Language Models with Structural Knowledge. CoRR abs/2401.14819 (2024) - [i30]Dexiong Chen, Till Hendrik Schulz, Karsten M. Borgwardt:
Learning Long Range Dependencies on Graphs via Random Walks. CoRR abs/2406.03386 (2024) - 2023
- [i29]Dexiong Chen, Paolo Pellizzoni, Karsten M. Borgwardt:
Fisher Information Embedding for Node and Graph Learning. CoRR abs/2305.07580 (2023) - 2022
- [i28]Dexiong Chen, Leslie O'Bray, Karsten M. Borgwardt:
Structure-Aware Transformer for Graph Representation Learning. CoRR abs/2202.03036 (2022) - [i27]Carlos G. Oliver, Dexiong Chen, Vincent Mallet, Pericles Philippopoulos, Karsten M. Borgwardt:
Approximate Network Motif Mining Via Graph Learning. CoRR abs/2206.01008 (2022) - [i26]Dexiong Chen, Bowen Fan, Carlos G. Oliver, Karsten M. Borgwardt:
Unsupervised Manifold Alignment with Joint Multidimensional Scaling. CoRR abs/2207.02968 (2022) - 2021
- [i25]Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten M. Borgwardt:
Topological Graph Neural Networks. CoRR abs/2102.07835 (2021) - [i24]Leslie O'Bray, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions. CoRR abs/2106.01098 (2021) - [i23]Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt:
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. CoRR abs/2107.05230 (2021) - [i22]Michael F. Adamer, Leslie O'Bray, Edward De Brouwer, Bastian Rieck, Karsten M. Borgwardt:
The magnitude vector of images. CoRR abs/2110.15188 (2021) - [i21]Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten M. Borgwardt:
Weisfeiler and Leman go Machine Learning: The Story so far. CoRR abs/2112.09992 (2021) - 2020
- [i20]Michael Moor, Max Horn, Christian Bock, Karsten M. Borgwardt, Bastian Rieck:
Path Imputation Strategies for Signature Models. CoRR abs/2005.12359 (2020) - [i19]Bastian Rieck, Tristan Yates, Christian Bock, Karsten M. Borgwardt, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy:
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. CoRR abs/2006.07882 (2020) - [i18]Jannis Born, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, Karsten M. Borgwardt:
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis. CoRR abs/2009.06116 (2020) - [i17]Karsten M. Borgwardt, M. Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck:
Graph Kernels: State-of-the-Art and Future Challenges. CoRR abs/2011.03854 (2020) - 2019
- [i16]Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten M. Borgwardt:
Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis. CoRR abs/1902.01659 (2019) - [i15]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, Karsten M. Borgwardt, Gunnar Rätsch, Tobias M. Merz:
Machine learning for early prediction of circulatory failure in the intensive care unit. CoRR abs/1904.07990 (2019) - [i14]Michael Moor, Max Horn, Bastian Rieck, Karsten M. Borgwardt:
Topological Autoencoders. CoRR abs/1906.00722 (2019) - [i13]Matteo Togninalli, M. Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten M. Borgwardt:
Wasserstein Weisfeiler-Lehman Graph Kernels. CoRR abs/1906.01277 (2019) - [i12]Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten M. Borgwardt:
Set Functions for Time Series. CoRR abs/1909.12064 (2019) - 2018
- [i11]Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten M. Borgwardt:
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. CoRR abs/1812.09764 (2018) - 2017
- [i10]Mahito Sugiyama, Karsten M. Borgwardt:
Significant Pattern Mining on Continuous Variables. CoRR abs/1702.08694 (2017) - 2015
- [i9]Felipe Llinares-López, Laetitia Papaxanthos, Dean A. Bodenham, Karsten M. Borgwardt:
Searching for significant patterns in stratified data. CoRR abs/1508.05803 (2015) - 2014
- [i8]Mahito Sugiyama, Felipe Llinares-López, Niklas Kasenburg, Karsten M. Borgwardt:
Significant Subgraph Mining with Multiple Testing Correction. CoRR abs/1407.0316 (2014) - [i7]Felipe Llinares, Mahito Sugiyama, Karsten M. Borgwardt:
Identifying Higher-order Combinations of Binary Features. CoRR abs/1407.1176 (2014) - 2013
- [i6]Aasa Feragen, Jens Petersen, Dominik G. Grimm, Asger Dirksen, Jesper Johannes Holst Pedersen, Karsten M. Borgwardt, Marleen de Bruijne:
Geometric tree kernels: Classification of COPD from airway tree geometry. CoRR abs/1303.7390 (2013) - 2012
- [i5]Dominik G. Grimm, Bastian Greshake, Stefan Kleeberger, Christoph Lippert, Oliver Stegle, Bernhard Schölkopf, Detlef Weigel, Karsten M. Borgwardt:
easyGWAS: An integrated interspecies platform for performing genome-wide association studies. CoRR abs/1212.4788 (2012) - 2009
- [i4]Karsten M. Borgwardt, Zoubin Ghahramani:
Bayesian two-sample tests. CoRR abs/0906.4032 (2009) - 2008
- [i3]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample Problem. CoRR abs/0805.2368 (2008) - [i2]S. V. N. Vishwanathan, Karsten M. Borgwardt, Imre Risi Kondor, Nicol N. Schraudolph:
Graph Kernels. CoRR abs/0807.0093 (2008) - 2007
- [i1]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised Feature Selection via Dependence Estimation. CoRR abs/0704.2668 (2007)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2026-07-19 01:47 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated, funded, and maintained by:
Schloss Dagstuhl - Leibniz Center for Informatics is a member of the Leibniz Association and is funded by the federal government of Germany and the states of Germany, especially Saarland and Rhineland‑Palatinate.
