Publications

Liu, X. et al. (2020) “Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension”, Lancet Digital Health, 2(10), pp. e537 - e548.

Cruz Rivera, S. et al. (2020) “Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension”, Lancet Digital Health, 2(10), pp. e549 - e560.

Chan, A.-W. et al. (2020) “Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension”, Nature Medicine, 26(9), pp. 1364–1374.

Xu, J. et al. (2020) “MetaFun: meta-learning with iterative functional updates”, Proceedings of Machine Learning Research , 119, pp. 10617–10627.

Zhou, Y. et al. (2020) “Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support”, in ICML 2020. ICML Proceedings.

Richards, D., Rebeschini, P. and Rosasco, L. (2020) “Decentralised Learning with Random Features and Distributed Gradient Descent”, arXiv.

Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable feature allocation models with sublinear growth of the feature sizes”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, pp. 3208–3218.

Foster, A. et al. (2020) “A unified stochastic gradient approach to designing Bayesian-optimal experiments”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. PMLR, pp. 2959–2969.

Wu, F. and Rebeschini, P. (2020) “Hadamard Wirtinger Flow for Sparse Phase Retrieval”, arXiv.