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Publications

Richards, D. and Rebeschini, P. (2019) “Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up”, arXiv.
Nalisnick, E. et al. (2019) “Do deep generative models know what they don’t know?”, in International Conference on Learning Representations.
Merel, J. et al. (2019) “Neural probabilistic motor primitives for humanoid control”, in International Conference on Learning Representations.
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. ML Research Press, 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. PMLR, pp. 758–767.
Hejblum, B. et al. (2019) “Sequential Dirichlet process mixtures of multivariate skew t-distributions for model-based clustering of flow cytometry data”, Annals of Applied Statistics, 13(1), pp. 638–660.
Galashov, A. et al. (2019) “Meta-Learning surrogate models for sequential decision making.”
Ayed, F. and Caron, F. (2019) “Nonnegative Bayesian nonparametric factor models with completely random measures for community detection”, arXiv.
Webb, S. et al. (2019) “A statistical approach to assessing neural network robustness”, in Seventh International Conference on Learning Representations (ICLR 2019). International Conferences on Learning Representations.
Webb, S. et al. (2019) “A statistical approach to assessing neural network robustness”, in Seventh International Conference on Learning Representations (ICLR 2019). International Conferences on Learning Representations.