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Publications

Holmes, C. and Walker, S. (2023) “Statistical inference with exchangeability and martingales”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2247), p. 20220143.
Naik, C. et al. (2023) “Bayesian nonparametrics for sparse dynamic networks”, in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Springer Nature, pp. 191–206.
Naik, C. et al. (2023) “Bayesian nonparametrics for sparse dynamic networks”, in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Springer Nature, pp. 191–206.
Naik, C. et al. (2023) “Bayesian Nonparametrics for Sparse Dynamic Networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature, pp. 191–206.
Naik, C. et al. (2023) “Bayesian Nonparametrics for Sparse Dynamic Networks”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature, pp. 191–206.
Johnson, E., Pike-Burke, C. and Rebeschini, P. (2023) “Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes”, arXiv.
Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets”, arXiv.
Lu, Y., Reinert, G. and Cucuringu, M. (2023) “Co-Trading Networks for Modeling Dynamic Interdependency Structures and Estimating High-Dimensional Covariances in US Equity Markets”, SSRN Electronic Journal.
Caron, F. et al. (2023) “Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning”, arXiv.
Alfano, C., Yuan, R. and Rebeschini, P. (2023) “A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence”, arXiv.