{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpretable-distribution-features-with","title":"Interpretable Distribution Features with Maximum Testing Power","arxiv_id":"1605.06796","date":"2016-05-22","proceeding":"NeurIPS 2016 12","authors":["Wittawat Jitkrittum","Zoltan Szabo","Kacper Chwialkowski","Arthur Gretton"],"abstract":"Two semimetrics on probability distributions are proposed, given as the sum\nof differences of expectations of analytic functions evaluated at spatial or\nfrequency locations (i.e, features). The features are chosen so as to maximize\nthe distinguishability of the distributions, by optimizing a lower bound on\ntest power for a statistical test using these features. The result is a\nparsimonious and interpretable indication of how and where two distributions\ndiffer locally. An empirical estimate of the test power criterion converges\nwith increasing sample size, ensuring the quality of the returned features. In\nreal-world benchmarks on high-dimensional text and image data, linear-time\ntests using the proposed semimetrics achieve comparable performance to the\nstate-of-the-art quadratic-time maximum mean discrepancy test, while returning\nhuman-interpretable features that explain the test results.","url_abs":"http://arxiv.org/abs/1605.06796v2","url_pdf":"http://arxiv.org/pdf/1605.06796v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interpretable-distribution-features-with","repo_url":"https://github.com/wittawatj/interpretable-test","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.06796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.06796"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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