Research

Main interests

My main research interests are Gaussian processes modelling, uncertainty quantification, Bayesian set estimation and Monte Carlo methods. I worked on efficient computational methods for conditional simulations, Monte Carlo methods for orthant probabilities of high dimensional Gaussian vectors and adaptive design of experiments.

Below you will find a list of pre-prints and publications. Also check out my google scholar for the most up-to-date list.

Pre-prints

A. Benavoli, D. Azzimonti, D. Piga. A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes. 2020 [bib, arXiv]

D. Azzimonti, M. Schürch, A. Benavoli, M. Zaffalon. Orthogonally Decoupled Variational Fourier Features. 2020 [bib, arXiv]

Publications

D. Azzimonti, C. Rottondi, A. Giusti, M. Tornatore and A. Bianco. Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]. IEEE/OSA Journal of Optical Communications and Networking, 13(1):A56–A66. 2021 [bib, link]

A. Benavoli, D. Azzimonti, D. Piga. Skew Gaussian Processes for Classification. Machine Learning. 2020 [bib, arXiv, link]

M. Schürch, D. Azzimonti, A. Benavoli and M. Zaffalon. Recursive Estimation for Sparse Gaussian Process Regression. Automatica, 120, 109127. 2020 [bib, arXiv, link]

D. Azzimonti, C. Rottondi and M. Tornatore. Reducing probes for quality of transmission estimation in optical networks with active learning. J. Opt. Commun. Netw., 12(1):A38–A48. 2020 [bib, link]

D. Azzimonti, D. Ginsbourger, C. Chevalier, J. Bect, and Y. Richet. Adaptive design of experiments for conservative estimation of excursion sets. Technometrics, 63:1, 13-26. 2021 [bib, arXiv, link]

D. Azzimonti, D. Ginsbourger, J. Rohmer, and D. Idier. Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. Technometrics, 61:4, 474-493. 2019 [bib, arXiv, link]

D. Azzimonti, and D. Ginsbourger. Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation. Journal of Computational and Graphical Statistics, 27(2):255–267, 2018. [bib, arXiv, link]

D. Azzimonti, J. Bect, C. Chevalier, and D. Ginsbourger. Quantifying uncertainties on excursion sets under a Gaussian random field prior. SIAM/ASA Journal on Uncertainty Quantification, 4(1):850–874, 2016. [bib, arXiv, link ]

D. Azzimonti, D. Jeulin, and F. Willot. Optical properties of deposit models for paints: full-fields FFT computations and representative volume element. Journal of Modern Optics, 60(7):519–528, 2013. [bib, arXiv, link]

Conference papers

L. Kania, M. Schürch, D. Azzimonti, A. Benavoli. Sparse Information Filter for Fast Gaussian Process Regression. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2021. 2021. [bib, link]

A. Benavoli, D. Azzimonti, D. Piga. Preferential Bayesian optimisation with Skew Gaussian Processes. In 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21Companion), July 10–14, 2021, Lille, France, New York, NY, USA. ACM. 2021 [bib, arXiv, link]

G. Corani, J. P. S. C. Augusto, D. Azzimonti, M. Zaffalon. Probabilistic Reconciliation of Hierarchical Forecast via Bayes’ Rule. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020. 2020 [ bib, arXiv, link]

D. Azzimonti, C. Rottondi, A. Giusti, M. Tornatore, A. Bianco. Active vs Transfer Learning Approaches for QoT Estimation with Small Training Datasets. Optical Fiber Communication Conference (OFC) 2020, OSA Technical Digest (Optical Society of America, 2020), paper M4E.1. [bib, link]

D. Azzimonti. Two types of Bayesian excursion set estimates based on Gaussian process models. 21st European Young Statisticians Meeting. 2019 [bib, link]

D. Azzimonti, C. Rottondi, and M. Tornatore. Using Active Learning to Decrease Probes for QoT Estimation in Optical Networks. Optical Fiber Communication Conference (OFC) 2019, OSA Technical Digest (Optical Society of America, 2019), paper Th1H.1. [bib, link]

D. Azzimonti, D. Ginsbourger, C. Chevalier, and Y. Richet. Conservative estimates of excursion sets in reliability engineering. Proceedings of 47 Journées de Statistique de la SFdS, 2015. [bib, link]

Thesis

D. Azzimonti. Contributions to Bayesian set estimation relying on random field priors. Ph.D. thesis, University of Bern, 2016. [bib, link]