Research

Preprints

• Harrison Zhu, Carles Balsells Rodas, Yingzhen Li (2022). Markovian Gaussian Process Variational Autoencoders. Workshop on Continuous Time Methods for Machine Learning, International Conference on Machine Learning. [Manuscript]
• Alexander Pondaven, Märt Bakler, Donghu Guo, Hamzah Hashim, Martin Ignatov, Harrison Zhu (2022). Convolutional Neural Processes for Inpainting Satellite Images. ArXiv. [Manuscript]
• Harrison Zhu, Adam Howes, Owen van Eer, Maxime Rischard, Dino Sejdinovic and Seth Flaxman (2021). Aggregated Gaussian Processes with Multiresolution Earth Observation Covariates. ArXiv. [Manuscript] [Code]

Papers

• Xing Liu, Harrison Zhu, Jean-François Ton, George Wynne, Andrew Duncan (2022). Grassmann Stein Variational Gradient Descent. 25th International Conference on Artificial Intelligence and Statistics. [Manuscript] [Code]
• Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman and François-Xavier Briol (2020). Bayesian probabilistic numerical integral with tree based priors. 34th Conference on Advances in Neural Information Processing Systems 2020. [Manuscript] [Code]
• Seth Flaxman, Swapnil Mishra, Axel Gandy, H Juliette T Unwin, Thomas A Mellan, Helen Coupland, Charles Whittaker, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Mélodie Monod, Azra C Ghani, Christl A Donnelly, Steven M Riley, Michaela AC Vollmer, Neil M Ferguson, Lucy C Okell, Samir Bhatt (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature pp1-8. [Manuscript] [Code]

Discussions

• Harrison Zhu, Xing Liu, Alberto Caron, Ioanna Manolopoulou, Seth Flaxman and François-Xavier Briol (2020). Contributed Discussion of “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”. Bayesian Analysis, 15(3), to appear. [Journal (pp55-58)] [Manuscript Code]

Talks and Presentations

• Sparse Variational Gaussian Processes - 12 November 2021. CSML Reading Group, Imperial College London. [Slides] [Demo]

Notes and Extracts

Here are some random notes that I thought would be useful for a casual read. A lot of them contain mistakes and so please read them with your own caution.

• An Overview of Sparse Gaussian Processes

Code

• neural-processes: a PyTorch implementation of various Neural Processes

Side Projects

• [Covid-19 Age Specific Mortality Data]. An automated pipeline for collecting age-specific COVID-19 data.
• IC Hack 19, an application for live spatio-temporal modelling of temperatures and precipitation in the USA