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Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing

NeurIPS 2019 · Learning theory and algorithms › Kernels and regression theory · approach: Kernel two-sample testing · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Optimize test locations and Gaussian width rather than use randomly drawn locations with grid-searched width (ablation)L1-ME · Fast food: McDo vs Burger KingType-II error: 0.426 -> 0.112Helped
Optimize test locations and Gaussian width rather than use randomly drawn locations with grid-searched width (ablation)L1-SCF · Fast food: McDo vs Burger KingType-II error: 0.960 -> 0.428Helped
Use L1 geometry instead of the L2 mean-embedding testMean-embedding two-sample test · Fast food: McDo vs Wendy'sType-II error: 0.416 -> 0.156Helped
Optimize test locations and Gaussian width rather than use randomly drawn locations with grid-searched width (ablation)L1-SCF · Fast food: McDo vs Arby'sType-II error: 0.468 -> 0.006Helped
Optimize test locations and Gaussian width rather than use randomly drawn locations with grid-searched width (ablation)L1-ME · Fast food: McDo vs Taco BellType-II error: 0.624 -> 0.554Helped
Use L1 geometry rather than L2 geometryKernel two-sample tests · Synthetic GMD, GVD, and Blobstest powerHelped
Optimize test locations rather than use random locations (ablation)L1-ME and L1-SCF · Synthetic GMD, GVD, and Blobstest powerHelped
Use SCF features instead of mean-embedding featuresL1 test · Synthetic Blobstest powerHelped
Use L1-opt-SCF instead of quadratic-time MMDTwo-sample test · Synthetic GVDtest powerHelped
Use L1-opt-SCF instead of quadratic-time MMDTwo-sample test · Synthetic Blobstest powerHelped
Use optimized L1 tests rather than their L2 counterpartsKernel two-sample test · HiggsType-II errorHelped
Optimize locations rather than use grid-based random locations (ablation)L1-ME and L1-SCF · HiggsType-II errorHelped

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