Wittawat Jitkrittum

New preprint: Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. In this work, we augment GAN’s loss function with a new term that provides more information about what the generator learns during training.

I spoke at the Symposium on frontier research in information science and technology at VISTEC, Thailand. Topic: Recent Advances in Kernel Methods for Model Criticism. Slides.

We have released Python code for “Informative Features for Model Comparison”.

New preprint: Large sample analysis of the median heuristic. In this work, we theoretically study the commonly used median heuristic for setting the bandwidth of a Gaussian kernel.

Our NIPS paper “Informative Features for Model Comparison” is now online on Arxiv. Python code.

Our paper titled “Informative Features for Model Comparison” was accepted to NIPS 2018. Given two samples from two candidate models, and a reference sample (observed data), the goal is to design a statistical test to determine which of the two samples is closer to the reference sample. This has an application for comparing two GAN models.

I was invited to speak at Department of Mathematics and Computer Science, Chulalongkorn University. Topic: A Technical Introduction to Kernel Goodness-of-Fit Testing.

I was invited to speak at VISTEC, Wangchan Valley, Rayong, Thailand. Topic: Machine Learning Fundamentals. Event details. Slides.

I was invited to give a talk at the BKK Machine Learning Meetup event. Topic: Introduction to kernel methods for comparing distributions. Slides.

I was invited to give a talk at the Workshop on Functional Inference and Machine Intelligence (19-21 Feb 2018), Tokyo. Slides.

The podcast we did with This Week in Machine Learning & AI on our NIPS Best Paper 2017 was published here.

I started working remotely as a postdoc at Max Planck Institute for Intelligent Systems with Bernhard Schölkopf.

I gave a 50-minute presentation on A Linear-Time Kernel Goodness-of-Fit Test (NIPS 2017 best paper). Download slides.

I gave an oral presentation at NIPS 2017 on A Linear-Time Kernel Goodness-of-Fit Test. The paper was selected for one of three best paper awards at NIPS 2017. Python code here. Presentation slides here. Talk video here.

Our paper A Linear-Time Kernel Goodness-of-Fit Test has been accepted for oral presentation at NIPS 2017. Python code.

I gave an oral presentation at ICML 2017 on our new linear-time independence test. The slides are here.

Uploaded slides for the linear-time nonparametric goodness-of-fit test.

We have released Python code for our linear-time nonparametric goodness-of-fit test.

I gave a talk on a new linear-time dependence measure at the UCL workshop on the theory of big data. See the slides here.

A new fast nonparametric goodness-of-fit test. See A Linear-Time Kernel Goodness-of-Fit Test.