12 citations · 44 across the 17 of their papers we have counts for
8 papers · 1 filter
Stationary MMD Points
Zonghao Chen, Toni Karvonen, Heishiro Kanagawa +2
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical integration. Several authors have proposed to se…
Controlling Moments with Kernel Stein Discrepancies
Heishiro Kanagawa, Alessandro Barp, Arthur Gretton +1
Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. No…
A kernel Stein test of goodness of fit for sequential models
Jerome Baum, Heishiro Kanagawa, Arthur Gretton
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 seq…
Blindness of score-based methods to isolated components and mixing proportions
Li K. Wenliang, Heishiro Kanagawa
Statistical tasks such as density estimation and approximate Bayesian inference often involve densities with unknown normalising constants. Score-based methods, including score mat…
Testing Goodness of Fit of Conditional Density Models with Kernels
Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf
We propose two nonparametric statistical tests of goodness of fit for conditional distributions: given a conditional probability density function and a joint sample, decid…
Amortised Learning by Wake-Sleep
Li K. Wenliang, Theodore Moskovitz, Heishiro Kanagawa +1
Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning f…
A Kernel Stein Test for Comparing Latent Variable Models
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey +2
We propose a kernel-based nonparametric test of relative goodness of fit, where the goal is to compare two models, both of which may have unobserved latent variables, such that the…
Informative Features for Model Comparison
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy +3
Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tes…