I am a postdoctoral researcher at Empirical Inference Department, Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. I work in the field of machine learning (intersection of computer science and statistics). My research topics include (but not limited to)
- Fast (linear runtime) non-parametric statistical tests
- Kernel-based representation of data
- Deep generative modelling of images
- Approximate Bayesian inference
- Please feel free to contact me for a research discussion.
- If you are a student working in a field related to machine learning, and are looking for an internship opportunity (for 2-6 months) at the Empirical Inference department, please contact me.
Contact: Wittawat Jitkrittum (วิทวัส จิตกฤตธรรม) ( )
Max Planck Institute for Intelligent Systems
Phone: +49 7071 601
|8 Jun 2019|
|16 May 2019||
Our paper “Kernel Mean Matching for Content Addressability of GANs” (ICML 2019) is now online on arXiv. The link is here. Code coming soon!
|7 May 2019||
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.
|22 Apr 2019||
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.
|11 Apr 2019|
|21 Mar 2019||
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.
|29 Jan 2019||
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.
|20 Dec 2018||
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.
|3 Nov 2018||
We have released Python code for “Informative Features for Model Comparison”.
|30 Oct 2018||
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.
|30 Oct 2018|
|7 Sep 2018||
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.
|21 Mar 2018||
I was invited to speak at Department of Mathematics and Computer Science, Chulalongkorn University. Topic: A Technical Introduction to Kernel Goodness-of-Fit Testing.
|16 Mar 2018|
|7 Mar 2018|
|20 Feb 2018||
I was invited to give a talk at the Workshop on Functional Inference and Machine Intelligence (19-21 Feb 2018), Tokyo. Slides.
|24 Jan 2018|
|1 Jan 2018||
I started working remotely as a postdoc at Max Planck Institute for Intelligent Systems with Bernhard Schölkopf.
|18 Dec 2017|
|5 Dec 2017||
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.
Last update: 13-Jun-19
Based on al-folio theme.