602 citations · 2.9k across the 103 of their papers we have counts for
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Fast Non-Parametric Tests of Relative Dependency and Similarity
Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus +3
We introduce two novel non-parametric statistical hypothesis tests. The first test, called the relative test of dependency, enables us to determine whether one source variable is s…
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann +4
We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test bas…
An Adaptive Test of Independence with Analytic Kernel Embeddings
Wittawat Jitkrittum, Zoltan Szabo, Arthur Gretton
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference between analytic embedd…
Large-Scale Kernel Methods for Independence Testing
Qinyi Zhang, Sarah Filippi, Arthur Gretton +1
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible framework for fully nonparametric hypothesis tests of independence, which can captur…
Interpretable Distribution Features with Maximum Testing Power
Wittawat Jitkrittum, Zoltan Szabo, Kacper Chwialkowski +1
Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, f…
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf +1
Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal vari…