K. Borgwardt

Publications192
h-index45
Citations15,990
Highly Influential Citations2,156
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  • Publications
  • Influence
A Kernel Two-Sample Test
TLDR
We propose a framework for analyzing and comparing distributions, which we use to construct statistical tests to determine if two samples are drawn from different distributions. Expand
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  • 524
  • PDF
A Kernel Method for the Two-Sample-Problem
TLDR
We propose two statistical tests to determine if two samples are from different distributions. Expand
  • 1,276
  • 285
  • PDF
Correcting Sample Selection Bias by Unlabeled Data
TLDR
We present a nonparametric method which directly produces resampling weights without distribution estimation. Expand
  • 1,220
  • 168
  • PDF
Weisfeiler-Lehman Graph Kernels
TLDR
We propose a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. Expand
  • 905
  • 165
  • PDF
Integrating structured biological data by Kernel Maximum Mean Discrepancy
MOTIVATION Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based… Expand
  • 695
  • 147
  • PDF
Efficient graphlet kernels for large graph comparison
TLDR
We propose to compare graphs by counting graphlets, i.e., subgraphs with k nodes where k ∈ {3, 4, 5}. Expand
  • 628
  • 78
  • PDF
Shortest-path kernels on graphs
  • K. Borgwardt, H. Kriegel
  • Mathematics, Computer Science
  • Fifth IEEE International Conference on Data…
  • 27 November 2005
TLDR
We propose graph kernels based on shortest paths, which are polynomial to compute, positive definite and retain expressivity while avoiding the phenomenon of ”tottering”. Expand
  • 686
  • 77
  • PDF
Whole-genome sequencing of multiple Arabidopsis thaliana populations
The plant Arabidopsis thaliana occurs naturally in many different habitats throughout Eurasia. As a foundation for identifying genetic variation contributing to adaptation to diverse environments, a… Expand
  • 897
  • 73
Protein function prediction via graph kernels
TLDR
We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. Expand
  • 617
  • 60
  • PDF
Covariate Shift by Kernel Mean Matching
This chapter contains sections titled: Introduction, Sample Reweighting, Distribution Matching, Risk Estimates, The Connection to Single Class Support Vector Machines, Experiments, Conclusion,… Expand
  • 365
  • 55
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