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Publications

Richards, D., Rebeschini, P. and Rosasco, L. (2020) “Decentralised Learning with Random Features and Distributed Gradient Descent”, arXiv.
Ayed, F., Lee, J. and Caron, F. (2020) “The Normal-Generalised Gamma-Pareto process: A novel pure-jump Lévy process with flexible tail and jump-activity properties”, arXiv.
Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable feature allocation models with sublinear growth of the feature sizes”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, pp. 3208–3218.
Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable feature allocation models with sublinear growth of the feature sizes”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, pp. 3208–3218.
Foster, A. et al. (2020) “A unified stochastic gradient approach to designing Bayesian-optimal experiments”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. PMLR, pp. 2959–2969.
Wu, F. and Rebeschini, P. (2020) “Hadamard Wirtinger Flow for Sparse Phase Retrieval”, arXiv.
Bloem-Reddy, B. and Teh, Y. (2020) “Probabilistic symmetries and invariant neural networks”, Journal of Machine Learning Research, 21(90), p. 1−61.
Tomašev, N. et al. (2020) “AI for social good: unlocking the opportunity for positive impact”, Nature Communications, 11(1).
Behr, M. et al. (2020) “Testing for dependence on tree structures”, Proceedings of the National Academy of Sciences of the United States of America, 117(18), pp. 9787–9792.
Banerjee, A. et al. (2020) “Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study”, The Lancet, 395(10238), pp. 1715–1725.