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University of Oxford, Department of Statistics logo
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Publications

Le, T. et al. (2018) “Auto-encoding sequential Monte Carlo”, in Sixth International Conference on Learning Representations (ICLR), Vancouver Canada, 30th April - 3rd May, 2018. OpenReview.
Wegner, A. et al. (2018) “Identifying networks with common organizational principles”, Journal of Complex Networks, 6(6), pp. 887–913.
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.
Rainforth, T. et al. (2018) “On Nesting Monte Carlo Estimators”, in Proceedings of Machine Learning Research, pp. 4267–4276.
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.
Rainforth, T. et al. (2018) “Tighter Variational Bounds are Not Necessarily Better”, in Proceedings of Machine Learning Research, pp. 4277–4285.
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.