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Publications

Alfano, C. et al. (2024) “Learning mirror maps in policy mirror descent”, arXiv.
Coppock, H. et al. (2024) “Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers”, Nature Machine Intelligence, 6(2), pp. 229–242.
Coppock, H. et al. (2024) “Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers”, Nature Machine Intelligence, 6(2), pp. 229–242.
Ouyang, R. et al. (2024) “L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders”, arXiv.
Fong, E., Holmes, C. and Walker, S. (2024) “Authors’ reply to the Discussion of ‘Martingale Posterior Distributions’”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 85(5), pp. 1413–1416.
Fong, E., Holmes, C. and Walker, S. (2024) “Martingale posterior distributions”, Journal of the Royal Statistical Society Series B (Statistical Methodology), 85(5), pp. 1357–1391.
Rivera, S. et al. (2024) “Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI”, BULL PAN AM HEALTH ORGAN, 48, p. e12.
Deligiannidis, G., de Bortoli, V. and Doucet, A. (2024) “Quantitative uniform stability of the iterative proportional fitting procedure”, The Annals of Applied Probability, 34(1A).
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2024) “Sample-efficiency in multi-batch reinforcement learning: the need for dimension-dependent adaptivity”, in Proceedings of the International Conference on Learning Representations (ICLR 2024). OpenReview.
Miao, N., Teh, Y. and Rainforth, T. (2024) “SelfCheck: using LLMs to zero-shot check their own step-by-step reasoning”, in The Twelfth International Conference on Learning Representations ICLR 2024. International Conference on Learning Representations.