Timezone: »
We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests for unnormalized densities. The KSD is defined by its Stein operator: current operators used in testing apply to fixed-dimensional spaces. As our main contribution, we extend the KSD to the variable-dimension setting by identifying appropriate Stein operators, and propose a novel KSD goodness-of-fit test. As with the previous variants, the proposed KSD does not require the density to be normalized, allowing the evaluation of a large class of models. Our test is shown to perform well in practice on discrete sequential data benchmarks.
Author Information
Jerome Baum (University College London, University of London)
Heishiro Kanagawa (Newcastle University, UK)
Arthur Gretton (Gatsby Computational Neuroscience Unit)
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 -
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: Amortised Learning by Wake-Sleep »
Li Kevin Wenliang · Theodore Moskovitz · Heishiro Kanagawa · Maneesh Sahani -
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 -
2017 Poster: An Adaptive Test of Independence with Analytic Kernel Embeddings »
Wittawat Jitkrittum · Zoltan Szabo · Arthur Gretton -
2017 Talk: An Adaptive Test of Independence with Analytic Kernel Embeddings »
Wittawat Jitkrittum · Zoltan Szabo · Arthur Gretton