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Publications

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.
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.
Sharma, M. et al. (2024) “Incorporating Unlabelled Data into Bayesian Neural Networks”, Transactions on Machine Learning Research, 2024.
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.
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.
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.
Mantziou, A. et al. (2023) “The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights”, Journal of Complex Networks, 11(6).
Huh, J. and Rebeschini, P. (2023) “Generalization bounds for label noise stochastic gradient descent”, arXiv.