Publications

Sodjo, J. et al. (2019) “A Bayesian Nonparametric Model for Unsupervised Joint Segmentation of a Collection of Images”, IEEE Access, 7, pp. 120176–120188.

Ayed, F., Lee, J. and Caron, F. (2019) “Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior”, in 36th International Conference on Machine Learning, ICML 2019, pp. 604–613.

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.

Nalisnick, E. et al. (2019) “Do deep generative models know what they don’t know?”, 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.

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].

Doherty, A. et al. (2018) “GWAS identifies 14 loci for device-measured physical activity and sleep duration”, Nature Communications, 9.

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

Xu, X. and Reinert, G. (2018) “Triad-based comparison and signatures of directed networks”, in Studies in Computational Intelligence. Springer, pp. 590–602.