Talks

Information Theoretic Meta Learning with Gaussian Processes. UAI (2021).

Functional Regularisation for Continual Learning with Gaussian Processes. Statistics Seminar, Barcelona School of Economics (2020), slides.

Gradient-based Adaptive Markov Chain Monte Carlo. 2th Symposium on Advances in Approximate Bayesian Inference (2019), slides.

Gradient-based Adaptive Markov Chain Monte Carlo. Cambridge University, Department of Engineering (2019).

The Hamming Ball Sampler. 3rd Meeting on Statistics (2015), slides.

Distributed Kernel Representations for Variational Sparse Gaussian Processes. Gaussian Process Approximations Workshop (2015), slides.

Variational Inference for Gaussian and Determinantal Point Processes. Advances in Variational Inference NIPS 2014 Workshop, slides.

Doubly Stochastic Variational Bayes for non-Conjugate Inference. ICML (2014), video lecture.

Bayesian Gaussian Process Latent Variable Model. AISTATS (2010), slides, video lecture.

Variational Model Selection for Sparse Gaussian Process Regression. BARK (2008), slides, video lecture.

Markov Chain Monte Carlo Algorithms for Gaussian Processes. Newton Institute workshop (2008), slides, video lecture.

Gaussian process modelling of transcription factor networks using Markov Chain Monte-Carlo. LICSB (2008), slides, video lecture.