Timezone: »
A new computationally efficient dependence measure, and an adaptive statistical test of independence, are proposed. The dependence measure is the difference between analytic embeddings of the joint distribution and the product of the marginals, evaluated at a finite set of locations (features). These features are chosen so as to maximize a lower bound on the test power, resulting in a test that is data-efficient, and that runs in linear time (with respect to the sample size n). The optimized features can be interpreted as evidence to reject the null hypothesis, indicating regions in the joint domain where the joint distribution and the product of the marginals differ most. Consistency of the independence test is established, for an appropriate choice of features. In real-world benchmarks, independence tests using the optimized features perform comparably to the state-of-the-art quadratic-time HSIC test, and outperform competing O(n) and O(n log n) tests.
Author Information
Wittawat Jitkrittum (UCL)
Zoltan Szabo (École Polytechnique)
[CV](http://www.cmap.polytechnique.fr/~zoltan.szabo/ZoltanSzabo_CV.pdf)
Arthur Gretton (Gatsby Computational Neuroscience Unit)
Related Events (a corresponding poster, oral, or spotlight)
-
2017 Poster: An Adaptive Test of Independence with Analytic Kernel Embeddings »
Wed. Aug 9th 08:30 AM -- 12:00 PM Room Gallery #111
More from the Same Authors
-
2022 : Adapting to Shifts in Latent Confounders via Observed Concepts and Proxies »
Matt Kusner · Ibrahim Alabdulmohsin · Stephen Pfohl · Olawale Salaudeen · Arthur Gretton · Sanmi Koyejo · Jessica Schrouff · Alexander D'Amour -
2023 : Prediction under Latent Subgroup Shifts with High-dimensional Observations »
William Walker · Arthur Gretton · Maneesh Sahani -
2023 Poster: A Kernel Stein Test of Goodness of Fit for Sequential Models »
Jerome Baum · Heishiro Kanagawa · Arthur Gretton -
2022 Poster: Importance Weighted Kernel Bayes' Rule »
Liyuan Xu · Yutian Chen · Arnaud Doucet · Arthur Gretton -
2022 Spotlight: Importance Weighted Kernel Bayes' Rule »
Liyuan Xu · Yutian Chen · Arnaud Doucet · Arthur Gretton -
2021 Poster: Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction »
Afsaneh Mastouri · Yuchen Zhu · Limor Gultchin · Anna Korba · Ricardo Silva · Matt J. Kusner · Arthur Gretton · Krikamol Muandet -
2021 Spotlight: Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction »
Afsaneh Mastouri · Yuchen Zhu · Limor Gultchin · Anna Korba · Ricardo Silva · Matt J. Kusner · Arthur Gretton · Krikamol Muandet -
2020 Poster: Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data »
Tamara Fernandez · Arthur Gretton · Nicolas Rivera · Wenkai Xu -
2020 Poster: Learning Deep Kernels for Non-Parametric Two-Sample Tests »
Feng Liu · Wenkai Xu · Jie Lu · Guangquan Zhang · Arthur Gretton · D.J. Sutherland -
2019 : Invited Talk - Arthur Gretton: Relative goodness-of-fit tests for models with latent variables. »
Arthur Gretton -
2019 Poster: Learning deep kernels for exponential family densities »
Li Kevin Wenliang · D.J. Sutherland · Heiko Strathmann · Arthur Gretton -
2019 Oral: Learning deep kernels for exponential family densities »
Li Kevin Wenliang · D.J. Sutherland · Heiko Strathmann · Arthur Gretton