Publications

Abdalla, M. et al. (2018) “A general framework for predicting the transcriptomic consequences of non-coding variation.”

Kim, H. and Teh, Y. (2018) “Scaling up the automatic statistician: Scalable structure discovery using gaussian processes”, in International Conference on Artificial Intelligence and Statistics, AISTATS 2018, pp. 575–584.

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.

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.

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.