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Publications

Rowland, M. et al. (2018) “An analysis of categorical distributional reinforcement learning”, in International Conference on Artificial Intelligence and Statistics, AISTATS 2018, pp. 29–37.
Gamelo, M. et al. (2018) “Conditional neural processes”, in 35th International Conference on Machine Learning, ICML 2018, pp. 2738–2747.
Schwarz, J. et al. (2018) “Progress & compress: A scalable framework for continual learning”, in 35th International Conference on Machine Learning, ICML 2018, pp. 7199–7208.
Woodroffe, R. and Donnelly, C. (2018) “European badgers and the control of bovine tuberculosis in the United Kingdom”, in Biology and Conservation of Musteloids, pp. 410–419.
Webb, S. et al. (2018) “Faithful inversion of generative models for effective amortized inference”, in Advances in Neural Information Processing Systems.
Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.
Rukat, T., Holmes, C. and Yau, C. (2018) “Probabilistic Boolean Tensor Decomposition”, in Proceedings of Machine Learning Research, pp. 4413–4422.
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), pp. e0006102 - e0006102-.
Maddison, C. et al. (2017) “Filtering variational objectives”, in Advances in Neural Information Processing Systems. Neural Information Processing Systems Foundation.