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.

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.

Our paper A Linear-Time Kernel Goodness-of-Fit Test has been accepted for oral presentation at NIPS 2017. Python code.

I gave an oral presentation at ICML 2017 on our new linear-time independence test. The slides are here.

Uploaded slides for the linear-time nonparametric goodness-of-fit test.

We have released Python code for our linear-time nonparametric goodness-of-fit test.

I gave a talk on a new linear-time dependence measure at the UCL workshop on the theory of big data. See the slides here.

A new fast nonparametric goodness-of-fit test. See A Linear-Time Kernel Goodness-of-Fit Test.

Our paper: An Adaptive Test of Independence with Analytic Kernel Embeddings is accepted to ICML 2017.

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

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

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

Interpretable Distribution Features with Maximum Testing Power: a linear-time nonparametric two-sample test which returns a set of local features indicating why the null hypothesis is rejected. Python code available on Github.

K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings: summary statistic free approximate Bayesian computation with kernel embeddings. Accepted to AISTATS 2016.

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

Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM): a probabilistic model for non-linear manifold discovery.

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages: a fast, online algorithm for nonparametric learning of EP message updates. Source code available here.