Publications
Elliott, A. et al. (2019) “Anomaly detection in networks with application to financial transaction networks”, JournalName [Preprint].
Nalisnick, E. et al. (2019) “Hybrid models with deep and invertible features”, in 36th International Conference on Machine Learning, ICML 2019, pp. 8295–8304.
Zhou, Y. et al. (2019) “LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models”, in Proceedings of Machine Learning Research, pp. 148–157.
Webb, S. et al. (2019) “A statistical approach to assessing neural network robustness”, in 7th International Conference on Learning Representations, ICLR 2019.
Webb, S. et al. (2019) “A statistical approach to assessing neural network robustness”, in 7th International Conference on Learning Representations, ICLR 2019.
Mathieu, E. et al. (2019) “Disentangling disentanglement in variational autoencoders”, in 36th International Conference on Machine Learning, ICML 2019, pp. 7744–7754.
Goliński, A., Wood, F. and Rainforth, T. (2019) “Amortized Monte Carlo integration”, in 36th International Conference on Machine Learning, ICML 2019, pp. 4163–4172.
Mathieu, E. et al. (2019) “Disentangling disentanglement in variational autoencoders”, in 36th International Conference on Machine Learning, ICML 2019, pp. 7744–7754.
Fong, E., Lyddon, S. and Holmes, C. (2019) “Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap”, in Proceedings of Machine Learning Research, pp. 1952–1962.
Ayed, F., Lee, J. and Caron, F. (2019) “Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior”, in Proceedings of Machine Learning Research, pp. 395–404.