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Malte Rasch

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2 papers

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AAAI Conference 2007 Conference Paper

A Kernel Approach to Comparing Distributions

Arthur Gretton
Malte Rasch

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert Space. We apply this technique to construct a two-sample test, which is used for determining whether two sets of observations arise from the same distribution. We use this test in attribute matching for databases using the Hungarian marriage method, where it performs strongly. We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.

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NeurIPS Conference 2006 Conference Paper

A Kernel Method for the Two-Sample-Problem

Arthur Gretton
Karsten Borgwardt
Malte Rasch
Bernhard Schölkopf
Alex Smola

We propose two statistical tests to determine if two samples are from different dis- tributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The ﬁrst test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic. The test statistic can be com- puted in O(m2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly. We also demonstrate excellent performance when compar- ing distributions over graphs, for which no alternative tests currently exist.

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