People
Tom Ryder
- Email: t.ryder2@ncl.ac.uk
PhD title
Variational inference with applications to stochastic processes
Variational Inference provides a way to evaluate and sample from hard-to-compute probability densities. With the modern advances in GPU technologies, such techniques are faster and more powerful than ever before.
Using variational inference, we perform full probabilistic inference, beginning on stochastic differential equations, and approximate the conditioned diffusion process and parameter posteriors.
Supervisor
Publications
Black-Box Variational Inference for Stochastic Differential Equations - Ryder, T. Golightly, A. McGough, S. Prangle, D. - 35th International Conference on Machine Learning - Stockholm, Sweden - July 2018
Black-Box Autoregressive Density Estimation for State-Space Models - Ryder, T. Golightly, A. McGough, S. Prangle, D. - Bayesian Deep Learning workshop, NeurIPS 2018 - November 2018
Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes - Ryder, T. Ward, W.O. Prangle, D. Alvarez, A.M. - 2019 Conference on Neural Information Processing Systems - Vancouver, Canada - December 2019
Scalable approximate inference for state space models with normalising flows - Ryder, T. Golightly, A. Matthews, I. Prangle, D. - October 2019