Publications

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.

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.

Schwessinger, R. et al. (2019) “DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning.”

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.

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.