I am a fourthyear PhD student working with Arthur Gretton at Gatsby Computational Neuroscience Unit, UCL. I am expected to finish my PhD study in September 2017. I received M.Eng. from Tokyo Institute of Technology where I worked with Masashi Sugiyama on supervised feature selection using squaredloss mutual information. Before that I was a research assistant working with Thanaruk Theeramunkong on a Thai news relations discovery project. I received B.Sc. in Computer Science from SIIT, Thammasat university, Thailand.
Contact: Wittawat Jitkrittum (วิทวัส จิตกฤตธรรม) ( )
My works are listed on this page. I occasionally update my blog summarizing what I learn. Some photos I have taken are on Flickr. I also maintain a web site for Gatsby's machine learning journal club.
News

Nov 2016 . A new paper. Cognitive Bias in Ambiguity Judgements: Using Computational Models to Dissect the Effects of Mild Mood Manipulation in Humans in PLOS One.

Oct 2016. An Adaptive Test of Independence with Analytic Kernel Embeddings, a fast nonparametric independence test. Python code here.

Aug 2016. Interpretable Distribution Features with Maximum Testing Power is accepted to NIPS 2016 as a full oral presentation. See our 2minute introduction video here.

May 2016. Interpretable Distribution Features with Maximum Testing Power: a lineartime nonparametric twosample test which returns a set of local features indicating why the null hypothesis is rejected. Python code available on Github.

Dec 2015. K2ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings: summary statistic free approximate Bayesian computation with kernel embeddings. Accepted to AISTATS 2016.

Nov 2015. We released the code for Locally Linear Latent Variable Model (LLLVM), which was accepted to NIPS 2015. Check our Matlab code here on Github.

Jun 2015. Bayesian Manifold Learning: Locally Linear Latent Variable Model (LLLVM): a probabilistic model for nonlinear manifold discovery.

Mar 2015. KernelBased JustInTime Learning for Passing Expectation Propagation Messages: a fast, online algorithm for nonparametric learning of EP message updates. Source code available here.