Lorenzo Pacchiardi

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

   Abs Bib HTML

   Probabilistic forecasting consists of stating a probability distribution for a future outcome based on past observations. In meteorology, ensembles of physics-based numerical models are run to get such distribution. Usually, performance is evaluated with scoring rules, functions of the forecast distribution and the observed outcome. With some scoring rules, calibration and sharpness of the forecast can be assessed at the same time. In deep learning, generative neural networks parametrize distributions on high-dimensional spaces and easily allow sampling by transforming draws from a latent variable. Conditional generative networks additionally constrain the distribution on an input variable. In this manuscript, we perform probabilistic forecasting with conditional generative networks trained to minimize scoring rule values. In contrast to Generative Adversarial Networks (GANs), no discriminator is required and training is stable. We perform experiments on two chaotic models and a global dataset of weather observations; results are satisfactory and better calibrated than what achieved by GANs.

   @article{pacchiardi2021probabilistic,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization},
     author = {Pacchiardi, Lorenzo and Adewoyin, Rilwan and Dueben, Peter and Dutta, Ritabrata},
     journal = {arXiv preprint arXiv:2112.08217},
     year = {2021},
     eprint = {2112.08217},
     archiveprefix = {arXiv},
     url = {https://arxiv.org/abs/2112.08217},
     html = {https://arxiv.org/abs/2112.08217},
     primaryclass = {stat.ML},
     selected = {true}
   }

2. arXiv

   Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators

   Pacchiardi, Lorenzo, and Dutta, Ritabrata

   arXiv preprint arXiv:2104.03889 2021

   Abs Bib HTML Code

   We propose a framework for Bayesian Likelihood-Free Inference (LFI) based on Generalized Bayesian Inference using scoring rules (SR). SR are used to evaluate probabilistic models given an observation; a proper SR is minimised in expectation when the model corresponds to the true data generating process for the observation. Using a strictly proper SR, for which the above minimum is unique, ensures posterior consistency of our method. As the likelihood function is intractable for LFI, we employ consistent estimators of SR using model simulations in a pseudo-marginal MCMC; we show the target of such chain converges to the exact SR posterior with increasing number of simulations. Furthermore, we note popular LFI techniques like Bayesian Synthetic Likelihood (BSL) and semiparametric BSL can be seen as special cases of our framework using only proper (but not strictly so) SR. We provide empirical results validating our consistency result and show how related approaches do not enjoy this property. Practically, we use the Energy and Kernel Scores, but our general framework sets the stage for extensions with other scoring rules.

   @article{pacchiardi2021generalized,
     abbr = {arXiv},
     bibtex_show = {true},
     title = {Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators},
     author = {Pacchiardi, Lorenzo and Dutta, Ritabrata},
     journal = {arXiv preprint arXiv:2104.03889},
     year = {2021},
     eprint = {2104.03889},
     archiveprefix = {arXiv},
     html = {https://arxiv.org/abs/2104.03889},
     url = {https://arxiv.org/abs/2104.03889},
     primaryclass = {stat.ME},
     code = {https://github.com/LoryPack/GenBayes_LikelihoodFree_ScoringRules},
     selected = {true}
   }

3. JMLR

   Score Matched Neural Exponential Families for Likelihood-Free Inference

   Pacchiardi, Lorenzo, and Dutta, Ritabrata

   Journal of Machine Learning Research 2022

   Abs Bib HTML Code

   Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations. In Approximate Bayesian Computation (ABC), a popular LFI method, summary statistics are used to reduce data dimensionality. ABC algorithms adaptively tailor simulations to the observation in order to sample from an approximate posterior, whose form depends on the chosen statistics. In this work, we introduce a new way to learn ABC statistics: we first generate parameter-simulation pairs from the model independently on the observation; then, we use Score Matching to train a neural conditional exponential family to approximate the likelihood. The exponential family is the largest class of distributions with fixed-size sufficient statistics; thus, we use them in ABC, which is intuitively appealing and has state-of-the-art performance. In parallel, we insert our likelihood approximation in an MCMC for doubly intractable distributions to draw posterior samples. We can repeat that for any number of observations with no additional model simulations, with performance comparable to related approaches. We validate our methods on toy models with known likelihood and a large-dimensional time-series model.

   @article{pacchiardi2020score,
     abbr = {JMLR},
     bibtex_show = {true},
     author = {Pacchiardi, Lorenzo and Dutta, Ritabrata},
     title = {Score Matched Neural Exponential Families for Likelihood-Free Inference},
     journal = {Journal of Machine Learning Research},
     year = {2022},
     volume = {23},
     number = {38},
     pages = {1-71},
     html = {http://jmlr.org/papers/v23/21-0061.html},
     url = {http://jmlr.org/papers/v23/21-0061.html},
     code = {http://github.com/LoryPack/SM-ExpFam-LFI},
     selected = {true}
   }

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

   Abs Bib HTML Code

   ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy. We give an overview of the design of ABCpy and provide a performance evaluation concentrating on parallelization. This points us towards the inherent imbalance in some of the ABC algorithms. We develop a dynamic scheduling MPI implementation to mitigate this issue and evaluate the various ABC algorithms according to their adaptability towards high-performance computing.

   @article{JSSABCpy,
     abbr = {JSS},
     bibtex_show = {true},
     title = {ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation},
     author = {Dutta, Ritabrata and Schoengens, Marcel and Pacchiardi, Lorenzo and Ummadisingu, Avinash and Widmer, Nicole and K{\"u}nzli, Pierre and Onnela, Jukka-Pekka and Mira, Antonietta},
     volume = {100},
     html = {https://www.jstatsoft.org/index.php/jss/article/view/v100i07},
     url = {https://www.jstatsoft.org/index.php/jss/article/view/v100i07},
     doi = {10.18637/jss.v100.i07},
     number = {7},
     journal = {Journal of Statistical Software},
     year = {2021},
     pages = {1--38},
     code = {https://github.com/eth-cscs/abcpy},
     selected = {true}
   }