Skip to Main content Skip to Navigation
Conference papers

B-tests: Low Variance Kernel Two-Sample Tests

Abstract : A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced. Members of the test family are called Block-tests or B-tests, since the test statistic is an average over MMDs computed on subsets of the samples. The choice of block size allows control over the tradeoff between test power and computation time. In this respect, the $B$-test family combines favorable properties of previously proposed MMD two-sample tests: B-tests are more powerful than a linear time test where blocks are just pairs of samples, yet they are more computationally efficient than a quadratic time test where a single large block incorporating all the samples is used to compute a U-statistic. A further important advantage of the B-tests is their asymptotically Normal null distribution: this is by contrast with the U-statistic, which is degenerate under the null hypothesis, and for which estimates of the null distribution are computationally demanding. Recent results on kernel selection for hypothesis testing transfer seamlessly to the B-tests, yielding a means to optimize test power via kernel choice.
Document type :
Conference papers
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-00842098
Contributor : Matthew Blaschko <>
Submitted on : Monday, February 10, 2014 - 2:51:58 PM
Last modification on : Wednesday, July 15, 2020 - 2:11:57 PM
Long-term archiving on: : Sunday, April 9, 2017 - 10:21:07 AM

Files

nips2013.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00842098, version 3
  • ARXIV : 1307.1954

Collections

Citation

Wojciech Zaremba, Arthur Gretton, Matthew Blaschko. B-tests: Low Variance Kernel Two-Sample Tests. Neural Information Processing Systems, Dec 2013, Lake Tahoe, United States. ⟨hal-00842098v3⟩

Share

Metrics

Record views

546

Files downloads

934