Publications

Willetts, M., Roberts, S. and Holmes, C. (2020) “Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels”, in. Institute of Electrical and Electronics Engineers (IEEE), pp. 5286–5295.

Wu, F. and Rebeschini, P. (2020) “A continuous-time mirror descent approach to sparse phase retrieval”, in Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Neural Information Processing Systems Foundation, Inc., pp. 1–12.

Vaškevičius, T., Kanade, V. and Rebeschini, P. (2020) “The statistical complexity of early-stopped mirror descent”, in. Neural Information Processing Systems Foundation, Inc., pp. 1–12.

Kendall, M. et al. (2020) “Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis”, The Lancet Digital Health, 2(12), pp. e658 - e666.

Crook, O. et al. (2020) “Moving Profiling Spatial Proteomics Beyond Discrete Classification”, Proteomics, 20(23), p. e1900392.

Watson, J. and Holmes, C. (2020) “Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error”, Trials, 21(1), p. 156.

Watson, J. and Holmes, C. (2020) “Correction to: Graphing and reporting heterogeneous treatment effects through reference classes”, Trials, 21(1), p. 865.

Watson, J. and Holmes, C. (2020) “Graphing and reporting heterogeneous treatment effects through reference classes”, Trials, 21(1), p. 386.

Shin, J. et al. (2020) “Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers”, Nature Communications, 11(1), p. 5987.

Richards, D., Rebeschini, P. and Rosasco, L. (2020) “Decentralised learning with distributed gradient descent and random features”, in Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, pp. 8105–8115.