This page contains selected talks I have given.


Dec 2019 NeurIPS 2019 Tutorial: Interpretable Comparison of Distributions and Models

NeurIPS 2019.

Video. Slides (part 1, part 2, part 3).

Mar 2018 Machine Learning Fundamentals

VISTEC, Rayong, Thailand.

The first part of a series of four talks. Event details here.

Mar 2018 Introduction to Kernel Methods for Comparing Distributions

BKK Machine Learning Meetup, Bangkok.


Oct 2019 Informative Features for Model Comparison

Swiss Data Science Center (invited talk)

Slides. Talk recording.

Sep 2019 Informative Features for Comparing Distributions

DALI Meeting 2019

Feb 2018 A Linear-Time Kernel Goodness-of-Fit Test

Workshop on Functional Inference and Machine Intelligence, Tokyo.

Jun 2017 The Finite-Set Independence Criterion (FSIC)

3rd UCL Workshop on the Theory of Big Data, London.

Feb 2017 An Adaptive Test of Independence with Analytic Kernel Embeddings

Probabilistic Graphical Model Workshop 2017, Tokyo.

Dec 2016 Interpretable Distribution Features with Maximum Testing Power

NeurIPS 2016

May 2016 K2-ABC: Approximate Bayesian Computation with Kernel Embeddings


Dec 2015 Locally Linear Latent Variable Model


Apr 2015 Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
Feb 2012 Feature Selection via $\ell_1$-penalized Squared-loss Mutual Information

Tea Talks

Tea talks I gave at Max Planck Institute for Intelligent Systems and at Gatsby Unit, UCL when I was a PhD student.

Apr 2020 Integers and Divisibility
May 2017 Exact String Matching with Z-Array
Feb 2017 Some Counterexamples in Probability
Oct 2016 Support Points
Jul 2016 Least-Squares Two-Sample Test
Dec 2015 9 Matlab Tricks that You Probably Want to Know
Oct 2015 Optimal Dating Strategy
Jul 2015 Support Vector Clustering
May 2016 Useful Software/Tricks You Should Know
Nov 2014 Public-key Cryptography with RSA
Sep 2014 True Online TD$(\lambda)$
Jun 2014 Local Fisher Discriminant Analysis
Apr 2014 Learning with Local and Global Consistency

Machine Learning Journal Club

Talks I gave at the machine learning journal club at Gatsby Unit as a PhD student.

Jan 2017 Examples are not Enough, Learn to Criticize! Criticism for Interpretability
Oct 2016 Determinantal Point Processes for Machine Learning
Feb 2016 Bayesian Indirect Inference Using a Parametric Auxiliary Model
Nov 2015 On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-shift Alternatives
Aug 2015 Landmarking Manifolds with Gaussian Processes
May 2015 Deep Exponential Families
Apr 2015 Mean Field Methods
Feb 2015 Sum-Product, Bethe Approximation