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Publications

Miao, N. et al. (2023) “Learning Instance-Specific Augmentations by Capturing Local Invariances”, in Proceedings of Machine Learning Research, pp. 24720–24736.
Campbell, A. et al. (2023) “Trans-Dimensional Generative Modeling via Jump Diffusion Models”, in. Curran Associates, pp. 42217–42257.
Sharma, M. et al. (2023) “Do Bayesian Neural Networks Need To Be Fully Stochastic?”, in Proceedings of Machine Learning Research, pp. 7694–7722.
Bajaj, S. et al. (2023) “Understanding COVID-19 testing behaviour in England through a sociodemographic lens”, medRxiv.
Ivanova, D. et al. (2023) “CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design”, in Proceedings of Machine Learning Research, pp. 14445–14464.
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.”
Ayed, F., Lee, J. and Caron, F. (2022) “The Normal-Generalised Gamma-Pareto process: a novel pure-jump Levy process with flexible tail and jump-activity properties”, Bayesian Analysis, 19(1), pp. 123–152.
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.
Dupont, E. et al. (2022) “COIN++: neural compression across modalities”, Transactions on Machine Learning Research, 2022(11).