Publications

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.

Rukat, T., Holmes, C. and Yau, C. (2018) “Probabilistic Boolean Tensor Decomposition”, in Proceedings of Machine Learning Research, pp. 4413–4422.

Rainforth, T. et al. (2018) “Tighter Variational Bounds are Not Necessarily Better”, in Proceedings of Machine Learning Research, pp. 4277–4285.

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.

Webb, S. et al. (2018) “Faithful Inversion of Generative Models for Effective Amortized Inference”, in ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018).