Lorenzo Pacchiardi

Affiliations. Department of Statistics, University of Oxford

Department of Statistics

University of Oxford

24-29 St Giles'

Oxford OX1 3LB

United Kingdom

I am a PhD student in Statistics at the University of Oxford. I have mostly worked on Bayesian inference for likelihood-free models, exploiting deep learning tools and generalized Bayesian inference. More recently, I have become interested in probabilistic forecasting, specifically for the field of weather prediction. My supervisors are Dr. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford).

Prior to my PhD studies, I obtained a Bachelor degree in Physical Engineering from Politecnico di Torino (Italy). Afterwards, I studied towards an MSc in Physics of Complex Systems awarded by Politecnico di Torino and Université Paris-Sud, France. I have carried out my MSc thesis at LightOn, a machine learning startup in Paris.

news

Mar 16, 2022 I have created a website aimed at listing works on using Neural Networks in Bayesian Likelihood-Free Inference :nerd_face: I have added the papers I am aware of, but please contribute others you may know (see “About” page for how).
Feb 3, 2022 Our paper discussing a new method for learning summary statistics for Approximate Bayesian Computation has been published in Journal of Machine Learning Research! :champagne:
Dec 4, 2021 Our paper describing the likelihood-free inference package ABCpy has been published in the Journal of Statistical Software! :tada: Check here for a quick YouTube video introducing the library.
Aug 12, 2021 Our optimal lockdown paper has finally been published by PLOS Computational Biology! :tada: Check the media coverage here!
Jul 6, 2021 If you missed my contributed talk on my work on “Generalized Bayesian likelihood-free inference using scoring rules estimators” at ISBA 2021 World Meeting, you can find the pre-recorded video here on YouTube.

selected publications and preprints

  1. arXiv
    Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization
    Pacchiardi, Lorenzo, Adewoyin, Rilwan, Dueben, Peter, and Dutta, Ritabrata
    arXiv preprint arXiv:2112.08217 2021
  2. arXiv
    Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    arXiv preprint arXiv:2104.03889 2021
  3. JMLR
    Score Matched Neural Exponential Families for Likelihood-Free Inference
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    Journal of Machine Learning Research 2022
  4. JSS
    ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
    Dutta, Ritabrata, Schoengens, Marcel, Pacchiardi, Lorenzo, Ummadisingu, Avinash, Widmer, Nicole, Künzli, Pierre, Onnela, Jukka-Pekka, and Mira, Antonietta
    Journal of Statistical Software 2021