Publications by Probability

Mijoule, G. et al. (2023) “Stein’s density method for multivariate continuous distributions”, Electronic Journal of Probability, 28(none).

Shi, Q. and Winkel, M. (2023) “Two-sided immigration, emigration and symmetry prop- erties of self-similar interval partition evolutions”, ALEA-LATIN AMERICAN JOURNAL OF PROBABILITY AND MATHEMATICAL STATISTICS, 20, pp. 665–696.

He, Y. et al. (2023) “PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs”, in Proceedings of Machine Learning Research, pp. 121–1227.

Ayyer, A., Mandelshtam, O. and Martin, J. (2023) “The multispecies zero range process and modified Macdonald polynomials”, Seminaire Lotharingien de Combinatoire [Preprint], (89).

Addario-Berry, L. et al. (2023) “The Foata–Fuchs proof of Cayley’s formula, and its probabilistic uses”, Electronic Communications in Probability, 28(none).

Duchamps, J.-J., Foutel-Rodier, F. and Schertzer, E. (2023) “General epidemiological models: law of large numbers and contact tracing”, Electronic Journal of Probability, 28(none).

He, Y. et al. (2022) “MSGNN: a spectral graph neural network based on a novel magnetic signed Laplacian”, in Proceedings of the First Learning on Graphs Conference (LoG 2022). Journal of Machine Learning Research, pp. 40:1 – 40:39.

Fatima, A. and Reinert, G. (2022) “Stein’s method for distributions modelling competing and complementary risk problems.”

Etheridge, A., Gooding, M. and Letter, I. (2022) “On the effects of a wide opening in the domain of the (stochastic) Allen-Cahn equation and the motion of hybrid zones”, Electronic Journal of Probability, 27, pp. 1–53.

Clarkson, J. et al. (2022) “DAMNETS: a deep autoregressive model for generating Markovian network time series”, in Proceedings of the First Learning on Graphs Conference. Journal of Machine Learning Research, pp. 23:1 – 23:19.