Publications

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.

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

Holmes, C. and Watson, J. (2018) “Machine learning for randomised controlled trials: identifying treatment effectheterogeneity with strict control of type I error.”

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.

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.

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.

Dyson, L. et al. (2017) “Targeted treatment of yaws with household contact tracing: How much do we miss?”, American Journal Of Epidemiology, 187(4), pp. 837–844.

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.