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University of Oxford, Department of Statistics logo
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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.
Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.
Rainforth, T. et al. (2018) “On Nesting Monte Carlo Estimators”, in Proceedings of Machine Learning Research, pp. 4267–4276.
Rainforth, T. et al. (2018) “Tighter Variational Bounds are Not Necessarily Better”, in Proceedings of Machine Learning Research, pp. 4277–4285.
Rukat, T., Holmes, C. and Yau, C. (2018) “Probabilistic Boolean Tensor Decomposition”, in Proceedings of Machine Learning Research, pp. 4413–4422.
Ismail, S. et al. (2017) “A two-component copula with links to insurance”, Dependence Modeling, 5(1), pp. 295–303.
Flaxman, S., Teh, Y. and Sejdinovic, D. (2017) “Poisson intensity estimation with reproducing kernels”, Electronic Journal of Statistics, 11(2), pp. 5081–5104.
Maddison, C. et al. (2017) “Filtering variational objectives”, in Advances in Neural Information Processing Systems. Neural Information Processing Systems Foundation.
Coulson, M., Gaunt, R. and Reinert, G. (2017) “Compound Poisson approximation of subgraph counts in stochastic block models with multiple edges”, arXiv [Preprint].
Perrone, V. et al. (2017) “Poisson random fields for dynamic feature models”, Journal of Machine Learning Research, 18.