A Linear-Time Kernel Goodness-of-Fit Test with Wittawat Jitkrittum, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton – NIPS Best Paper ’17

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In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I then discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy!

In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy!

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About Arthur

About Wittawat

About Zoltan

About Kenji

Mentioned in the Interview

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