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Publications

Mathieu, E. et al. (2019) “Disentangling Disentanglement in Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 4402–4412.
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.
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.
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.
Ernst, M., Reinert, G. and Swan, Y. (2018) “Stein-type covariance identities: Klaassen, Papathanasiou and Olkin-Shepp type bounds for arbitrary target distributions”, JournalName [Preprint].