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Informative features for model comparison

Jitkrittum, W; Sangkloy, P; Schölkopf, B; Kanagawa, H; Hays, J; Gretton, A; (2018) Informative features for model comparison. In: Advances in Neural Information Processing Systems 31 (NIPS 2018). (pp. pp. 808-819). Neural Information Processing Systems Foundation, Inc.: Montréal, Canada. Green open access

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Abstract

Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tests which are nonparametric, computationally efficient (runtime complexity is linear in the sample size), and interpretable. As a unique advantage, our tests can produce a set of examples (informative features) indicating the regions in the data domain where one model fits significantly better than the other. In a real-world problem of comparing GAN models, the test power of our new test matches that of the state-of-the-art test of relative goodness of fit, while being one order of magnitude faster.

Type: Proceedings paper
Title: Informative features for model comparison
Event: 32nd International Conference on Neural Information Processing Systems
Location: Montréal, Canada
Dates: 2-8th December 2018
Open access status: An open access version is available from UCL Discovery
Publisher version: https://papers.nips.cc/paper/7361-informative-feat...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10076520
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