310 citations · 607 across the 39 of their papers we have counts for
5 papers · 1 filter
Kernel Distributionally Robust Optimization
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl +1
We propose kernel distributionally robust optimization (Kernel DRO) using insights from the robust optimization theory and functional analysis. Our method uses reproducing kernel H…
Learning Kernel Tests Without Data Splitting
Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf +1
Modern large-scale kernel-based tests such as maximum mean discrepancy (MMD) and kernelized Stein discrepancy (KSD) optimize kernel hyperparameters on a held-out sample via data sp…
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl +1
In order to anticipate rare and impactful events, we propose to quantify the worst-case risk under distributional ambiguity using a recent development in kernel methods -- the kern…
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 Conditional Moment Test via Maximum Moment Restriction
Krikamol Muandet, Wittawat Jitkrittum, Jonas Kübler
We propose a new family of specification tests called kernel conditional moment (KCM) tests. Our tests are built on a novel representation of conditional moment restrictions in a r…