AI RESEARCH
Heat and Mat\'ern Kernels on Matchings
arXiv CS.LG
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ArXi:2604.14331v1 Announce Type: new Applying kernel methods to matchings is challenging due to their discrete, non-Euclidean nature. In this paper, we develop a principled framework for constructing geometric kernels that respect the natural geometry of the space of matchings. To this end, we first provide a complete characterization of stationary kernels, i.e. kernels that respect the inherent symmetries of this space.