I am a research scientist at Google Research. Previously I was a postdoctoral researcher at Empirical Inference Department, Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf (from 2018 to 2020). 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
Contact: Wittawat Jitkrittum (วิทวัส จิตกฤตธรรม) ( )
|27 Jul 2021||
Our paper ABCDP: Approximate Bayesian Computation with Differential Privacy. is accepted to Entropy. This is one of the first works that studies differential privacy in the context of approximate Bayesian computation.
|14 May 2021||
A paper accepted at ICML 2021. Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. With collgeaues at Google Research.
|8 May 2021||
I gave a virtual, invited talk at Deep Learning and Artificial Intelligence Summer School 2021 (DLAI5) on the topic of comparing generative models.
|11 Feb 2021||
A new preprint “An Optimal Witness Function for Two-Sample Testing”. In this work, we explore a new way of constructing a witness function for two-sample testing in a data-driven manner. We show experimentally that one can take a kernel selected by an existing approach, and simply plug it into the new framework to get higher test power.
|1 Feb 2021|
|3 Dec 2020||
I gave a virtual seminar at IBM Research on kernel based model comparison. Thanks to Youssef Mroueh for inviting me.
|5 Nov 2020||
I gave a virtual seminar at EURECOM on kernel testing for comparing generative models. Thanks to Motonobu Kanagawa for inviting me.
|1 Aug 2020||
I will serve as a workflow chair for AISTATS 2021.
|27 Jul 2020||
We have released Python source code for our work “Testing Goodness of Fit of Conditional Density Models with Kernels” (in UAI 2020). Please check it out!
|10 Jul 2020||
Today is the last day of the virtual Machine Learning Summer School 2020. On behalf of the organizing team, I would like to thank all parties involved including our valued sponsors, our speakers, volunteers, and staff at the MPI-IS. All lectures are online. Please feel free to check here!
|15 May 2020|
|1 May 2020||
I joined Google Research as a research scientist. I would like to thank everyone who has supported me.
|2 Apr 2020||
A new preprint: Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem with J. J. Zhu and colleagues.
|25 Feb 2020||
Two new preprints, both tackling the problem of testing the goodness of fit of a conditional model. In the first work, the model is specified as an explicit conditional density function up to the normalizing constant. In the second work, the conditional model is specified implicitly in terms of a conditional moment function.
|2 Jan 2020||
I am co-organizing the Machine Learning Summer School (MLSS) 2020 at the Max Planck Institute for Intelligent Systems, Tübingen, Germany. Application is now open! Please apply. Deadline: 11 Feb 2020.
|10 Dec 2019|
|9 Dec 2019||
I will give a tutorial (with Dougal Sutherland and Arthur Gretton) at NeurIPS 2019 on Mon Dec 9th 11:15 - 13:15 @ West Hall A. The topic will be “Interpretable Comparison of Distributions and Models”. See the event here.
|29 Oct 2019|
Last update: 10-Aug-21
Based on al-folio theme.