Publications (Computational Statistics and Machine Learning) | Department of Statistics

Publications

Limnios, S. et al. (2023) “SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation”, arXiv.

Caron, F., Panero, F. and Rousseau, J. (2023) “On sparsity, power-law and clustering properties of graphex processes”, Advances in Applied Probability, 55(4), pp. 1211–1253.

Dhillon, G., Deligiannidis, G. and Rainforth, T. (2023) “On the expected size of conformal prediction sets”, in. Journal of Machine Learning Research, pp. 1549–1557.

Sinha, S. et al. (2023) “Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database”, European Journal of Cardio-Thoracic Surgery, 63(6), p. ezad183.

Mantziou, A. et al. (2023) “The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights”, arXiv.

Campbell, A. et al. (2023) “Trans-Dimensional Generative Modeling via Jump Diffusion Models”, arXiv.

Dunger‐Baldauf, C. et al. (2023) “Generating the Right Evidence at the Right Time: Principles of a New Class of Flexible Augmented Clinical Trial Designs”, Clinical Pharmacology & Therapeutics, 113(5), pp. 1132–1138.

Lee, J., Miscouridou, X. and Caron, F. (2023) “A unified construction for series representations and finite approximations of completely random measures”, Bernoulli, 29(3), pp. 2142–2166.

Gaunt, R. and Reinert, G. (2023) “Bounds for the chi-square approximation of Friedman’s statistic by Stein’s method”, Bernoulli - Journal of the Bernoulli Society, 29(3), pp. 2008–2034.

Falck, F. et al. (2023) “A multi-resolution framework for U-nets with applications to hierarchical VAEs”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 15529–15544.