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Publications

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.
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-.
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.
Jiang, X. et al. (2023) “Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk”, Nature Genetics, 55(11), pp. 1854–1865.
He, Y. et al. (2023) “Robust Angular Synchronization via Directed Graph Neural Networks”, arXiv.
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity”, arXiv.
Alfano, C., Yuan, R. and Rebeschini, P. (2023) “A novel framework for policy mirror descent with general parameterization and linear convergence”, in. Curran Associates.