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Publications

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.
Buttenschoen, M., Morris, G. and Deane, C. (2023) “PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences”, Chemical Science, 15(9), pp. 3130–3139.
Dablander, M. et al. (2023) “Exploring QSAR models for activity-cliff prediction.”, J. Cheminformatics, 15, pp. 47–47.
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).
Torabi, F. et al. (2023) “Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models”, Heliyon, 9(11), pp. e21734 - e21734-.
Huh, J. and Rebeschini, P. (2023) “Generalization bounds for label noise stochastic gradient descent”, arXiv.
Temčinas, T., Nanda, V. and Reinert, G. (2023) “Multivariate central limit theorems for random clique complexes”, Journal of Applied and Computational Topology, 8(6), pp. 1837–1880.
Temčinas, T., Nanda, V. and Reinert, G. (2023) “Multivariate central limit theorems for random clique complexes”, Journal of Applied and Computational Topology, 8(6), pp. 1837–1880.
He, Y. et al. (2023) “Robust Angular Synchronization via Directed Graph Neural Networks”, arXiv.