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Publications

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.
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.
Bloem-Reddy, B. and Teh, Y. (2020) “Probabilistic symmetries and invariant neural networks”, Journal of Machine Learning Research, 21(90), p. 1−61.
Wu, F. and Rebeschini, P. (2020) “Hadamard Wirtinger Flow for Sparse Phase Retrieval”, arXiv.
Rainforth, T. et al. (2020) “Target–aware Bayesian inference: how to beat optimal conventional estimators”, Journal of Machine Learning Research, 21(88), p. 1−54.
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.
Ernst, M., Reinert, G. and Swan, Y. (2020) “First order covariance inequalities via Stein’s method”, Bernoulli, 26(3), pp. 2051–2081.
Di Benedetto, G., Caron, F. and Teh, Y. (2020) “Non-exchangeable feature allocation models with sublinear growth of the feature sizes”, arXiv.