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 finishing PhD student in Statistics at the University of Oxford. During my PhD, I mostly worked on inference for generative models, spanning Bayesian simulation-based inference and training methods for generative neural networks. Some of the approaches I developed build on concepts from probabilistic forecasting. My supervisors are Dr. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford). I am now exploring the use of Large Language Models for conveying probabilistic information in complex tasks and am broadly interested in novel methods to quantify uncertainty (such as conformal prediction, normalizing flows and diffusion models).

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.


Dec 20, 2022 I won a travel award at the International Conference for Statistics and Data Science by the Institute of Mathematical Statistics. I really enjoyed the opportunity to present my work there!
Nov 17, 2022 On the 9th of November, I successfully defended my PhD thesis :champagne:. A profound thanks to my examiners Chris Holmes and Chris Oates for their insightful comments and suggestions.
Jun 3, 2022 I have two preprints on arXiv exploring an alternative to adversarial training to train generative networks; the first applies it to probabilistic forecasting, while the second is concerned with Likelihood-Free Inference. I feel that is a promising overlooked approach. Happy to hear any feedback! :smile:
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:

selected publications and preprints

  1. arXiv
    Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    arXiv preprint arXiv:2205.15784 2022
  2. arXiv
    Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
    Pacchiardi, Lorenzo, Adewoyin, Rilwan, Dueben, Peter, and Dutta, Ritabrata
    arXiv preprint arXiv:2112.08217 2022
  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