Publications

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.

Crook, O. et al. (2019) “A Bioconductor workflow for the Bayesian analysis of spatial proteomics”, p. 446.

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.”

Crook, O. et al. (2019) “Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics”, 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.

Rebeschini, P. and Tatikonda, S. (2019) “A new approach to Laplacian solvers and flow problems”, Journal of Machine Learning Research, 20(36), p. 1−37.

Geladaki, A. et al. (2019) “Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics”, Nature Communications, 10(1), p. 331.