Publications

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.

Menacher, A. et al. (2024) “Bayesian lesion estimation with a structured spike-and-slab prior”, Journal of the American Statistical Association [Preprint].

Clarkson, J. et al. (2024) “Split Conformal Prediction under Data Contamination”, in Proceedings of Machine Learning Research, pp. 5–27.

Sharma, M. et al. (2024) “Incorporating Unlabelled Data into Bayesian Neural Networks”, Transactions on Machine Learning Research, 2024.

Dauncey, S. et al. (2024) “Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection”, Transactions on Machine Learning Research, 2024.

Falck, F., Wang, Z. and Holmes, C. (2024) “Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective”, in Proceedings of Machine Learning Research, pp. 12784–12805.

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.

Benton, J., Deligiannidis, G. and Doucet, A. (2024) “Error Bounds for Flow Matching Methods”, Transactions on Machine Learning Research, 2024.

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.