Publications

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

Rainforth, T. et al. (2018) “On Nesting Monte Carlo Estimators”, in Proceedings of Machine Learning Research, pp. 4267–4276.

Le, T. et al. (2018) “Auto-encoding sequential Monte Carlo”, in 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.

Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.

Webb, S. et al. (2018) “Faithful inversion of generative models for effective amortized inference”, in Advances in Neural Information Processing Systems.

Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.

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.