310 citations · 607 across the 39 of their papers we have counts for
7 papers · 1 filter
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…
Kernel Stein Tests for Multiple Model Comparison
Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf +1
We address the problem of non-parametric multiple model comparison: given candidate models, decide whether each candidate is as good as the best one(s) or worse than it. We pro…
More Powerful Selective Kernel Tests for Feature Selection
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum +3
Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical a…
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…
Kernel Mean Matching for Content Addressability of GANs
Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal +3
We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users…
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…