ArXiv preprint, 2026
We develop a robust alternative to Wasserstein barycentres using optimal transport with Huber loss, establishing stability, existence, and robustness properties.
Recommended citation: C. Cardoso-Perelló, A. González-Sanz. Huber-Wasserstein barycenters for robust distribution-valued data , (2026). ArXiv preprint . https://arxiv.org/abs/2608.13131
ArXiV preprint, 2026
We study sharp small-regularisation asymptotics for a unified family of regularised optimal transport problems, including entropic and $L^p$-regularised optimal transport.
Recommended citation: C. Cardoso-Perelló, A. González-Sanz, M. Nutz. Sharp Asymptotics for Regularised Optimal Transport, (2026+). ArXiv preprint 2607.18191. https://arxiv.org/abs/2607.18191
MSc Thesis, 2024
I analyse a recent proof of the fact that a stationary, non-ergodic, quadratic and locally-optimal 2D Poisson matching does not exist.
Recommended citation: C. A.C.C. Perello. Non-existence of the 2D quadratic Poisson optimal matching, (2024).
Appeared in Stat, 2023
We fit a spatio-temporal Hawkes processes to the London Santander Cycle bike-sharing system and study the resulting model.
Recommended citation: F. Sanna Passino, Y. Che, C. A.C.C. Perello. Graph-based mutually exciting point processes for modelling event times in docked bike-sharing systems, (2023). https://onlinelibrary.wiley.com/doi/full/10.1002/sta4.660
Preprint (submitted for publication), 2023
We introduce a new adaptive importance sampling algorithm that uses optimisation to adapt the proposal distribution to obtain a tighter envelope of the target distribution.
Recommended citation: C. A.C.C. Perello, Akyildiz, Ö. D. Adaptively Optimised Adaptive Importance Samplers, (2023). https://arxiv.org/abs/2307.09341
Appeared in Bayesian Analysis, 2021
We fit beta splines to COVID-19 mortality data for each US state and use the fitted curves to estimate future casualties.
Recommended citation: Monod, M., Blenkinsop, A., Brizzi, A., Chen, Y., A.C.C. Perello, C., Jogarah, V., Wang, Y., Flaxman, S., Bhatt, S., & Ratmann, O. Regularised B-splines projected Gaussian Process priors to estimate time-trends of age-specific COVID-19 deaths related to vaccine roll-out, (2021). https://projecteuclid.org/journals/bayesian-analysis/volume-18/issue-3/Regularised-B-splines-Projected-Gaussian-Process-Priors-to-Estimate-Time/10.1214/22-BA1334.full