310 citations · 607 across the 39 of their papers we have counts for
15 papers · 1 filter
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…
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet +1
Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty i…
ABCDP: Approximate Bayesian Computation with Differential Privacy
Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum
We develop a novel approximate Bayesian computation (ABC) framework, ABCDP, that produces differentially private (DP) and approximate posterior samples. Our framework takes advanta…
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…
Fisher Efficient Inference of Intractable Models
Song Liu, Takafumi Kanamori, Wittawat Jitkrittum +1
Maximum Likelihood Estimators (MLE) has many good properties. For example, the asymptotic variance of MLE solution attains equality of the asymptotic Cram{é}r-Rao lower bound (effi…
A Linear-Time Kernel Goodness-of-Fit Test
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo +2
We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. We learn the test features that best indicate the differences between…
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…
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…
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess +4
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node,…
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a para…
Passing Expectation Propagation Messages with Kernel Methods
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess
We propose to learn a kernel-based message operator which takes as input all expectation propagation (EP) incoming messages to a factor node and produces an outgoing message. In or…
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar +3
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their…
Feature Selection via L1-Penalized Squared-Loss Mutual Information
Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to s…
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal +2
The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operato…