Efficient computation for Bayesian deep learning. MSR Cambridge & Oxford statistics, Mar 2018

Gradient estimators for implicit models. @ NIPS 2017 approximate inference workshop, Dec 2017

Wild approximate inference: why and how. @ UCL CSML seminar series, Dec 2017

Adversarial attacks and defences. @ AI safety reading group, CUED, Nov 2017

Approximate inference with amortised MCMC. @ ICML 2017 implicit generative model workshop, Aug 2017

Dropout-alpha BNNs. @ ICML 2017, Aug 2017

Approximate inference with amortised MCMC. CamAIML workshop, Mar 2017

Objective functions for variational auto-encoders. @ Twitter Cortex Vx (previously Magic Pony), Sept 2016

Variational inference with Rényi divergence. MSR-MLG joint workshop, Mar 2016

Alpha divergence back to conversation. Research Talk @ CBL, Feb 2016

Stochastic expectation propagation. Invited talk @ Tsinghua University, Dec 2015

Stochastic optimisation and adaptive learning rates. Reading Group Talk @ CBL (with Mark Rowland), Nov 2015 [code]

Concave-convex procedure. Tea Talk @ CBL, Oct 2015

Expectation propagation as a way of life. Tea Talk @ CBL, Feb 2015. [reference]

Introduction to transfer learning. Reading Group Talk @ CBL (with Pei-Hao Su), Jan 2015.

Bayesian learning for restricted Boltzmann machines. Research Talk @ CBL, Nov 2014.

On Restricted Boltzmann Machine Learning. Guest Talk @ Dept of Math, SYSU, Jun 2014.

Loopy Belief Propagation. RCC Talk @ CBL (with Alex Matthews), Apr 2014.

The Importance of Encoding. Tea Talk @ CBL, Feb 2014. [reference]

IT & ML: Channels, Quantizers, and Divergences. RCC Talk @ CBL (with Antonio Artés-Rodríguez), Jan 2014.