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A Kernel Two-Sample Test
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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A Kernel Method for the Two-Sample-Problem
We propose two statistical tests to determine if two samples are from different distributions. Expand
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Correcting Sample Selection Bias by Unlabeled Data
We present a nonparametric method which directly produces resampling weights without distribution estimation. Expand
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Weisfeiler-Lehman Graph Kernels
We propose a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. Expand
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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-basedExpand
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Efficient graphlet kernels for large graph comparison
We propose to compare graphs by counting graphlets, i.e., subgraphs with k nodes where k ∈ {3, 4, 5}. Expand
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Shortest-path kernels on graphs
  • K. Borgwardt, H. Kriegel
  • Mathematics, Computer Science
  • Fifth IEEE International Conference on Data…
  • 27 November 2005
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
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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, aExpand
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Protein function prediction via graph kernels
We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. Expand
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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
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