Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. - Alex Mansbridge; Gregory Barbour; Davide Piras; Christopher Frye; Ilya Feige; David Barber (CoRR, 2020)

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events. - Davide Piras; Alessio Spurio Mancini; Benjamin Joachimi; Michael P. Hobson (CoRR, 2021)

Discovering the building blocks of dark matter halo density profiles with neural networks. - Luisa Lucie-Smith; Hiranya V. Peiris; Andrew Pontzen; Brian Nord; Jeyan Thiyagalingam; Davide Piras (CoRR, 2022)

Fast and realistic large-scale structure from machine-learning-augmented random field simulations. - Davide Piras; Benjamin Joachimi; Francisco Villaescusa-Navarro (CoRR, 2022)

CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators. - Davide Piras; Alessio Spurio Mancini (CoRR, 2023)

A representation learning approach to probe for dynamical dark energy in matter power spectra. - Davide Piras; Lucas Lombriser (CoRR, 2023)

A robust estimator of mutual information for deep learning interpretability. - Davide Piras; Hiranya V. Peiris; Andrew Pontzen; Luisa Lucie-Smith; Ningyuan Guo; Brian Nord (Mach. Learn. Sci. Technol., 2023)

The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison. - Davide Piras; Alicja Polanska; Alessio Spurio Mancini; Matthew A. Price; Jason D. McEwen (CoRR, 2024)

Full-waveform earthquake source inversion using simulation-based inference. - A. A. Saoulis; Davide Piras; Alessio Spurio Mancini; Benjamin Joachimi; A. M. G. Ferreira (CoRR, 2024)

ΛCDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum. - Davide Piras; Laura Herold; Luisa Lucie-Smith; Eiichiro Komatsu (CoRR, 2025)

Anchors no more: Using peculiar velocities to constrain H0 and the primordial Universe without calibrators. - Davide Piras; Francesco Sorrenti; Ruth Durrer; Martin Kunz (CoRR, 2025)

Transfer learning for multifidelity simulation-based inference in cosmology. - A. A. Saoulis; Davide Piras; Niall Jeffrey; Alessio Spurio Mancini; A. M. G. Ferreira; Benjamin Joachimi (CoRR, 2025)