Publications

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.

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.

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.

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.

Kossen, J., Gal, Y. and Rainforth, T. (2024) “In-context learning learns label relationships but is not conventional learning”, in Proceedings of the 12th 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.

Menacher, A. et al. (2024) “Bayesian lesion estimation with a structured spike-and-slab prior”, Journal of the American Statistical Association, 119(545), pp. 66–80.

Campbell, A. et al. (2024) “Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design”, in Proceedings of Machine Learning Research, pp. 5453–5512.

Dhillon, G., Deligiannidis, G. and Rainforth, T. (2024) “On the Expected Size of Conformal Prediction Sets”, in Proceedings of Machine Learning Research, pp. 1549–1557.