Publications

Rukat, T., Holmes, C. and Yau, C. (2018) “Probabilistic Boolean tensor decomposition”, in 35th International Conference on Machine Learning, ICML 2018, pp. 7007–7020.

Lyddon, S., Walker, S. and Holmes, C. (2018) “Nonparametric learning from Bayesian models with randomized objective functions”, in Advances in Neural Information Processing Systems, pp. 2071–2081.

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.

Perrone, V. et al. (2017) “Poisson random fields for dynamic feature models”, Journal of Machine Learning Research, 18.

Coulson, M., Gaunt, R. and Reinert, G. (2017) “Compound Poisson approximation of subgraph counts in stochastic block models with multiple edges”, arXiv [Preprint].

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.