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Publications

Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.
Webb, S. et al. (2018) “Faithful inversion of generative models for effective amortized inference”, in Advances in Neural Information Processing Systems.
Flaxman, S., Teh, Y. and Sejdinovic, D. (2017) “Poisson intensity estimation with reproducing kernels”, Electronic Journal of Statistics, 11(2), pp. 5081–5104.
Clements, M. et al. (2017) “Interpreting ambiguous ‘trace’ results in Schistosoma mansoni CCA Tests: estimating sensitivity and specificity of ambiguous results with no gold standard”, PLOS Neglected Tropical Diseases, 11(12), p. e0006102.
Maddison, C. et al. (2017) “Filtering variational objectives”, in Advances in Neural Information Processing Systems. Neural Information Processing Systems Foundation.
Perrone, V. et al. (2017) “Poisson random fields for dynamic feature models”, Journal of Machine Learning Research, 18.
Rahmioglu, N. et al. (2017) “Variability of genome-wide DNA methylation and mRNA expression profiles in reproductive and endocrine disease related tissues”, Epigenetics, 12(10), pp. 897–908.
Di Benedetto, G., Caron, F. and Teh, Y. (2017) “Non-exchangeable random partition models for microclustering”, arXiv.
Hasenclver, L. et al. (2017) “Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server”, Journal of Machine Learning Research, 18(106), pp. 1–37.
Caron, F. and Fox, E. (2017) “Sparse graphs using exchangeable random measures”, Journal of the Royal Statistical Society Series B: Statistical Methodology, 79(5), pp. 1295–1366.