Karsten M. Borgwardt
56 publications
15 venues
H Index 28
Affiliation
Max Planck Institute of Biochemistry, Munich, GermanyETH Zurich, Department of Biosystems Science and Engineering
Links
DBLPGoogle Scholar
ORCID- https://www.bsse.ethz.ch/mlcb/karsten/profile-karsten.html
- https://mathgenealogy.org/id.php?id=122424
- https://en.wikipedia.org/wiki/Karsten_Borgwardt
- https://www.wikidata.org/entity/Q47461780
- https://d-nb.info/gnd/133236943
- https://dblp.uni-trier.de/pers/hd/b/Borgwardt:Karsten_Michael
| Name | Venue | Year | citations |
|---|---|---|---|
On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks.
|
NIPS/NeurIPS | 2024 | 0 |
| Fisher Information Embedding for Node and Graph Learning.
|
ICML | 2023 | 0 |
| FASM and FAST-YB: Significant Pattern Mining with False Discovery Rate Control.
|
ICDM | 2023 | 0 |
| ProteinShake: Building datasets and benchmarks for deep learning on protein structures.
|
NIPS/NeurIPS | 2023 | 0 |
| Unsupervised Manifold Alignment with Joint Multidimensional Scaling.
|
ICLR | 2023 | 0 |
| Weisfeiler and Leman go Machine Learning: The Story so far.
|
JMLR | 2023 | 0 |
| Structure-Aware Transformer for Graph Representation Learning.
|
ICML | 2022 | 22 |
| Topological Graph Neural Networks.
|
ICLR | 2022 | 0 |
| Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions.
|
ICLR | 2022 | 0 |
| Filtration Curves for Graph Representation.
|
KDD | 2021 | 8 |
| Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence.
|
NIPS/NeurIPS | 2020 | 21 |
| Set Functions for Time Series.
|
ICML | 2020 | 0 |
| Topological Autoencoders.
|
ICML | 2020 | 0 |
| Wasserstein Weisfeiler-Lehman Graph Kernels.
|
NIPS/NeurIPS | 2019 | 108 |
| Introduction to the special issue for the ECML PKDD 2019 journal track.
|
DMKD | 2019 | 0 |
| A Persistent Weisfeiler-Lehman Procedure for Graph Classification.
|
ICML | 2019 | 60 |
| A Wasserstein Subsequence Kernel for Time Series.
|
ICDM | 2019 | 2 |
| Finding Statistically Significant Interactions between Continuous Features.
|
IJCAI | 2019 | 0 |
| Introduction to the special issue for the ECML PKDD 2019 journal track.
|
MLJ | 2019 | 0 |
| Kernel Conditional Clustering.
|
ICDM | 2017 | 3 |
| Multi-view Spectral Clustering on Conflicting Views.
|
ECML/PKDD | 2017 | 9 |
| Finding significant combinations of features in the presence of categorical covariates.
|
NIPS/NeurIPS | 2016 | 29 |
| Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing.
|
KDD | 2015 | 50 |
| Halting in Random Walk Kernels.
|
NIPS/NeurIPS | 2015 | 66 |
| Significant Subgraph Mining with Multiple Testing Correction.
|
SDM | 2015 | 0 |
| Multi-Task Feature Selection on Multiple Networks via Maximum Flows.
|
SDM | 2014 | 10 |
| It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals.
|
NIPS/NeurIPS | 2013 | 74 |
| Scalable kernels for graphs with continuous attributes.
|
NIPS/NeurIPS | 2013 | 145 |
| Rapid Distance-Based Outlier Detection via Sampling.
|
NIPS/NeurIPS | 2013 | 115 |
| Measuring Statistical Dependence via the Mutual Information Dimension.
|
IJCAI | 2013 | 13 |
| A Kernel Two-Sample Test.
|
JMLR | 2012 | 3343 |
| Feature Selection via Dependence Maximization.
|
JMLR | 2012 | 341 |
| Two-locus association mapping in subquadratic time.
|
KDD | 2011 | 20 |
| Efficient inference in matrix-variate Gaussian models with \iid observation noise.
|
NIPS/NeurIPS | 2011 | 84 |
| Weisfeiler-Lehman Graph Kernels.
|
JMLR | 2011 | 1399 |
| Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning.
|
MLJ | 2011 | 0 |
| Graph Kernels.
|
JMLR | 2010 | 1 |
| Fast subtree kernels on graphs.
|
NIPS/NeurIPS | 2009 | 253 |
| Near-optimal Supervised Feature Selection among Frequent Subgraphs.
|
SDM | 2009 | 119 |
| The graphlet spectrum.
|
ICML | 2009 | 75 |
| Efficient graphlet kernels for large graph comparison.
|
AISTATS | 2009 | 827 |
| A kernel method for unsupervised structured network inference.
|
AISTATS | 2009 | 11 |
| Metropolis Algorithms for Representative Subgraph Sampling.
|
ICDM | 2008 | 156 |
| The skew spectrum of graphs.
|
ICML | 2008 | 61 |
| Future trends in data mining.
|
DMKD | 2007 | 196 |
| Supervised feature selection via dependence estimation.
|
ICML | 2007 | 327 |
| A dependence maximization view of clustering.
|
ICML | 2007 | 116 |
| Colored Maximum Variance Unfolding.
|
NIPS/NeurIPS | 2007 | 109 |
| A Kernel Approach to Comparing Distributions.
|
AAAI | 2007 | 42 |
| Pattern Mining in Frequent Dynamic Subgraphs.
|
ICDM | 2006 | 150 |
| Correcting Sample Selection Bias by Unlabeled Data.
|
NIPS/NeurIPS | 2006 | 1526 |
| A Kernel Method for the Two-Sample-Problem.
|
NIPS/NeurIPS | 2006 | 1738 |
| 3DString: a feature string kernel for 3D object classification on voxelized data.
|
CIKM | 2006 | 11 |
| Fast Computation of Graph Kernels.
|
NIPS/NeurIPS | 2006 | 154 |
| Joint Regularization.
|
ESANN | 2005 | 1 |
| Shortest-Path Kernels on Graphs.
|
ICDM | 2005 | 865 |