Sequential Generative Models: Some Basics & Advances. @ Generative modelling summer school, June 2023

Make Stein's Method "Great Again" for Generative modelling? @ Distance-based methods for machine learning workshop, June 2023 [video]

Introduction to Bayesian Neural Networks. @ ProbAI 2023 summer school, June 2023

Towards Causal Deep Generative Models for Sequential Data. @ Quarter on Causality "When Causal Inference Meets Statistical Analysis", Apr 2023

Variational Bayes for Deep Learning: Towards Function-Space Uncertainty Quantification. @ BayesComp, Mar 2023

Understanding Masked Pre-Training: Fundamentally Different? @ GenU 2022 workshop, Sept 2022

Introduction to Bayesian Neural Networks. @ ProbAI 2022 summer school, June 2022 [video][notes][demo1][demo2]

Inference with scores: slices, diffusions and flows. @ ICML 2021 INNF+ workshop, July 2021 [video]

EBM inference & learning: A personal story. @ ICLR 2021 EBM workshop, May 2021 [video] [panel]

From parametric models to Gaussian processes. @ UnderstandingDL talk series, Data @ University de Sao Paolo, Apr 2021 [video]

Understanding approximate inference in Bayesian neural networks: A joint talk. Mar 2021 (as moderator).

Advances in Approximate Inference. Tutorial @ NeurIPS 2020

On estimating epistemic uncertainty. @ NeurIPS 2019 Bayesian deep learning workshop, Dec 2019 [video]

How accurate is the uncertainty estimate from your Bayesian neural networks? @ MICCAI 2019 UNSURE workshop, Oct 2019

Bayesian neural networks: a function space view tour. @ Gaussian process summer school, Sept 2019 [video]

Gradient estimators for implicit models with Stein's method. @ ICML 2019 Stein's method workshop, June 2019 [video]

Variational implicit processes. @ ICML 2019, June 2019

On KL divergence and beyond. @ U Cambridge Faculty of English, Nov 2018

Meta-learning for stochastic gradient MCMC. @ UAI 2018 uncertainty in DL workshop, Aug 2018

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

Towards a unified variational framework for approximate Bayesian inference. Invited talk @ Tsinghua University, Dec 2015

Stochastic expectation propagation. Spotlight @ NIPS 2015, 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.