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.

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.

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.

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.

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].

Chen, J. et al. (2018) “Stochastic expectation maximization with variance reduction”, in Neural Information Processing Systems. Massachusetts Institute of Technology Press.