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Poster
AutoML Two-Sample Test
Jonas M. Kübler · Vincent Stimper · Simon Buchholz · Krikamol Muandet · Bernhard Schölkopf

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #536
in Poster Session 3 »

Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose usage can require specialized knowledge about two-sample testing. We use a simple test that takes the mean discrepancy of a witness function as the test statistic and prove that minimizing a squared loss leads to a witness with optimal testing power. This allows us to leverage recent advancements in AutoML. Without any user input about the problems at hand, and using the same method for all our experiments, our AutoML two-sample test achieves competitive performance on a diverse distribution shift benchmark as well as on challenging two-sample testing problems.

Keywords: AutoML · MMD · Two-Sample Test

Author Information

Jonas M. Kübler (AWS / MPI-IS)
Vincent Stimper (University of Cambridge)
Simon Buchholz (Max-Planck Institute)
Krikamol Muandet (Max Planck Institute for Intelligent Systems)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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