ABCpy: A high-performance computing perspective to approximate Bayesian computation R Dutta, M Schoengens, L Pacchiardi, A Ummadisingu, N Widmer, ... Journal of Statistical Software 100 (7), 1-38, 2021 | 30* | 2021 |
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic R Dutta, SN Gomes, D Kalise, L Pacchiardi PLoS Computational Biology 17 (8), e1009236, 2021 | 24 | 2021 |
Score matched neural exponential families for likelihood-free inference L Pacchiardi, R Dutta Journal of Machine Learning Research 23 (38), 1-71, 2022 | 23* | 2022 |
Generalized Bayesian Likelihood-Free Inference L Pacchiardi, S Khoo, R Dutta arXiv preprint arXiv:2104.03889, 2021 | 23 | 2021 |
Distance-learning for approximate Bayesian computation to model a volcanic eruption L Pacchiardi, P Künzli, M Schöngens, B Chopard, R Dutta Sankhya B 83, 288-317, 2021 | 15 | 2021 |
How to catch an ai liar: Lie detection in black-box llms by asking unrelated questions L Pacchiardi, AJ Chan, S Mindermann, I Moscovitz, AY Pan, Y Gal, ... arXiv preprint arXiv:2309.15840, 2023 | 14 | 2023 |
Likelihood-free inference with generative neural networks via scoring rule minimization L Pacchiardi, R Dutta arXiv preprint arXiv:2205.15784, 2022 | 14 | 2022 |
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization L Pacchiardi, RA Adewoyin, P Dueben, R Dutta Journal of Machine Learning Research 25 (45), 1-64, 2024 | 11* | 2024 |
Statistical inference in generative models using scoring rules L Pacchiardi University of Oxford, 2022 | 1 | 2022 |
David Huk, Lorenzo Pacchiardi, Ritabrata Dutta and Mark Steel's contribution to the Discussion of ‘Martingale posterior distributions’ by Fong, Holmes and Walker D Huk, L Pacchiardi, R Dutta, M Steel Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023 | | 2023 |