Wittawat Jitkrittum

A new preprint “A Kernel Stein Test for Comparing Latent Variable Models”. The kernel Stein discrepancy (KSD) test proposed in Liu et al., 2016 and Chwialkowski et al., 2016 allows one to test the goodness of fit an unnormalized differentiable density model. However, when the model is a latent-variable model with an intractable marginal density, the KSD test cannot be applied easily. In our new work, we extend the KSD test to allow goodness-of-fit testing of latent-variable models whose marginal densities may be intractable.

I will attend ICML 2019 (9-15 June 2019) and CVPR 2019 (16-20 June 2019). If you would like to meet and discuss research, please feel free to send me a message.

Our paper “Kernel Mean Matching for Content Addressability of GANs” (ICML 2019) is now online on arXiv. The link is here. Code coming soon!

I am a co-organizer of the first Machine Learning Research School (MLRS) 2019 to be held in Bangkok, Thailand on 4-11 August 2019. Application is now open. Deadline: 31 May 2019. All welcome to apply! See also our MLRS facebook page.

Our paper titled “Kernel Mean Matching for Content Addressability of GANs” has been accepted to ICML 2019. This work proposes a procedure which adds “content addressability” to any pre-trained implicit model (including GANs). Given a set of input images, the procedure can generate images (from the chosen implicit model) that are similar to the input images, without retraining the model.

Our short paper titled “Generate Semantically Similar Images with Kernel Mean Matching” has been accepted for oral presentation at Women in Computer Vision Workshop at CVPR 2019. Paper here.

I am a co-organizer of the first Southeast Asia Machine Learning (SEA ML) School to be held in Jakarta, Indonesia on 8-12 July 2019. Application is now open. Deadline: 20 April 2019. No registration fee for students.

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.