Publications

Foster, A. et al. (2019) “Variational Bayesian Optimal Experimental Design”, in.

Crook, O., Gatto, L. and Kirk, P. (2019) “Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics”, Statistical Applications in Genetics and Molecular Biology, 18(6), p. 20180065.

Martínez-Rubio, D., Kanade, V. and Rebeschini, P. (2019) “Decentralized cooperative stochastic bandits”, in Advances in Neural Information Processing Systems 32. Neural Information Processing Systems Foundation.

Richards, D. and Rebeschini, P. (2019) “Optimal statistical rates for decentralised non-parametric regression with linear speed-up”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Neural Information Processing Systems Foundation.

Foster, A. et al. (2019) “A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments.”

Fawsitt, C. et al. (2019) “Cost-effectiveness analysis of baseline testing for resistance-associated polymorphisms to optimize treatment outcome in genotype 1 noncirrhotic treatment-naïve patients with chronic hepatitis C virus”, Value in Health, 23(2), pp. 180–190.

Naderiparizi, S. et al. (2019) “Amortized Rejection Sampling in Universal Probabilistic Programming”, arXiv.

Glaire, M. et al. (2019) “Correction: Tumour-infiltrating CD8+ lymphocytes and colorectal cancer recurrence by tumour and nodal stage”, British Journal of Cancer, 121(9), pp. 807–807.

Liu, X. et al. (2019) “Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed”, Nature Medicine, 25(10), pp. 1467–1468.

Crook, O. et al. (2019) “PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds”, arXiv.