Publications

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

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.

Ouyang, R. et al. (2024) “L2G2G: A Scalable Local-to-Global Network Embedding with Graph Autoencoders”, in Complex Networks & Their Applications XII. Springer Nature, pp. 400–412.

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.

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

He, Y. et al. (2024) “ROBUST ANGULAR SYNCHRONIZATION VIA DIRECTED GRAPH NEURAL NETWORKS”, in 12th International Conference on Learning Representations, ICLR 2024.

Wilsenach, J. et al. (2024) “Graph models of brain state in deep anaesthesia reveal sink state dynamics of reduced spatiotemporal complexity”, bioRxiv.

Clivio, O., Feller, A. and Holmes, C. (2024) “Towards Representation Learning for Weighting Problems in Design-Based Causal Inference”, in Proceedings of Machine Learning Research, pp. 856–880.

Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Optimal convergence rate for exact policy mirror descent in discounted Markov decision processes”, in Advances in Neural Information Processing Systems. NeurIPS, pp. 76496–76524.

Kanade, V., Rebeschini, P. and Vaskevicius, T. (2023) “The statistical complexity of early-stopped mirror descent”, Information and Inference: A Journal of the IMA, 12(4), pp. 3010–3041.