Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem

Mar 31, 2020
Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf

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Kernel Conditional Moment Test via Maximum Moment Restriction

Mar 07, 2020
Krikamol Muandet, Wittawat Jitkrittum, Jonas Kübler

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Testing Goodness of Fit of Conditional Density Models with Kernels

Feb 24, 2020
Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf

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Kernel-Guided Training of Implicit Generative Models with Stability Guarantees

Nov 03, 2019
Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Schölkopf

* There was a misunderstanding in how an article should be updated on arXiv. We have withdrawn this article from this link. The same article can be found at arXiv:1901.09206 

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Kernel Stein Tests for Multiple Model Comparison

Oct 27, 2019
Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum

* Accepted to NeurIPS 2019 

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More Powerful Selective Kernel Tests for Feature Selection

Oct 14, 2019
Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira

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ABCDP: Approximate Bayesian Computation Meets Differential Privacy

Oct 11, 2019
Mijung Park, Wittawat Jitkrittum

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A Kernel Stein Test for Comparing Latent Variable Models

Jul 01, 2019
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton

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Kernel Mean Matching for Content Addressability of GANs

May 14, 2019
Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf

* Wittawat Jitkrittum and Patsorn Sangkloy contributed equally to this work 

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Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces

Jan 26, 2019
Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

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Large sample analysis of the median heuristic

Oct 30, 2018
Damien Garreau, Wittawat Jitkrittum, Motonobu Kanagawa

* 27 pages, 6 figures 

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Informative Features for Model Comparison

Oct 27, 2018
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton

* Accepted to NIPS 2018 

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Model Inference with Stein Density Ratio Estimation

May 18, 2018
Song Liu, Wittawat Jitkrittum, Carl Henrik Ek

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A Linear-Time Kernel Goodness-of-Fit Test

Oct 24, 2017
Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton

* Accepted to NIPS 2017 

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Interpretable Distribution Features with Maximum Testing Power

Oct 28, 2016
Wittawat Jitkrittum, Zoltan Szabo, Kacper Chwialkowski, Arthur Gretton

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An Adaptive Test of Independence with Analytic Kernel Embeddings

Oct 15, 2016
Wittawat Jitkrittum, Zoltan Szabo, Arthur Gretton

* 8 pages of main text 

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K2-ABC: Approximate Bayesian Computation with Kernel Embeddings

Dec 26, 2015
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic

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Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)

Dec 01, 2015
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani

* accepted to NIPS 2015 

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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

Jun 09, 2015
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

* accepted to UAI 2015. Correct typos. Add more content to the appendix. Main results unchanged 

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Passing Expectation Propagation Messages with Kernel Methods

Jan 02, 2015
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess

* Accepted to Advances in Variational Inference, NIPS 2014 Workshop 

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High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso

Aug 21, 2013
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama

* 18 pages. To appear in Neural Computation 

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Feature Selection via L1-Penalized Squared-Loss Mutual Information

Oct 06, 2012
Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama

* 25 pages 

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