Le, T. et al. (2019) “Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow”, in Proceedings of Machine Learning Research, pp. 1039–1049.

Mathieu, E. et al. (2019) “Disentangling disentanglement in variational autoencoders”, in 36th International Conference on Machine Learning, ICML 2019, pp. 7744–7754.

Maddison, C. et al. (2019) “Particle value functions”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.

Mitrovic, J., Sejdinovic, D. and Teh, Y. (2019) “Deep kernel machines via the kernel reparametrization trick”, in 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.

Lee, J. et al. (2019) “A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure”, in Proceedings of Machine Learning Research, pp. 758–767.

Webb, S. et al. (2019) “A statistical approach to assessing neural network robustness”, in 7th International Conference on Learning Representations Iclr 2019.

Maddison, C., Mnih, A. and Teh, Y. (2019) “The concrete distribution: A continuous relaxation of discrete random variables”, in 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.

Mitrovic, J., Sejdinovic, D. and Teh, Y. (2018) “Causal inference via Kernel deviance measures”, in Advances in Neural Information Processing Systems. Massachusetts Institute of Technology Press.