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Publications

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.
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.
Ayed, F., Lee, J. and Caron, F. (2019) “Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior”, arXiv.
Rainforth, T. et al. (2019) “On nesting Monte Carlo estimators”, in 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm Sweden, 10th - 15th July 2018. Proceedings of Machine Learning Research, pp. 4267–4276.