Wittawat Jitkrittum

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
Contact: Wittawat Jitkrittum (วิทวัส จิตกฤตธรรม) (
)
ArXiv CV DBLP Github Google Scholar LinkedIn Orcid ID ResearchGate Semantic Scholar Twitter
Address:
Wittawat Jitkrittum
Max Planck Institute for Intelligent Systems
Max-Planck-Ring 4
72076 Tuebingen
Germany
Phone: +49 (0) 7071 601 - 564
News
10 Dec 2019 |
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9 Dec 2019 |
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29 Oct 2019 |
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14 Oct 2019 |
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4 Sep 2019 |
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3 Sep 2019 |
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1 Sep 2019 |
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11 Aug 2019 |
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3 Jul 2019 |
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8 Jun 2019 |
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16 May 2019 |
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7 May 2019 |
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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 |
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. |
21 Mar 2019 |
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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 |
Our NIPS paper “Informative Features for Model Comparison” is now online on Arxiv. Python code. |
Last update: 11-Dec-19
Based on al-folio theme.