Posterior analysis of rare variants in Gibbs-type species sampling models. - Oriana Cesari; Stefano Favaro; Bernardo Nipoti (J. Multivar. Anal., 2014)

Discussion of "On simulation and properties of the stable law" by L. Devroye and L. James. - Stefano Favaro; Bernardo Nipoti (Stat. Methods Appl., 2014)

A hybrid sampler for Poisson-Kingman mixture models. - Maria Lomeli; Stefano Favaro; Yee Whye Teh (NIPS, 2015)

Are Gibbs-Type Priors the Most Natural Generalization of the Dirichlet Process? - Pierpaolo De Blasi; Stefano Favaro; Antonio Lijoi; Ramsés H. Mena; Igor Prünster; Matteo Ruggiero (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

On a class of σ-stable Poisson-Kingman models and an effective marginalized sampler. - Stefano Favaro; Maria Lomeli; Yee Whye Teh (Stat. Comput., 2015)

Neural Stochastic Differential Equations. - Stefano Peluchetti; Stefano Favaro (CoRR, 2019)

Infinitely deep neural networks as diffusion processes. - Stefano Peluchetti; Stefano Favaro (AISTATS, 2020)

Stable behaviour of infinitely wide deep neural networks. - Stefano Peluchetti; Stefano Favaro; Sandra Fortini (AISTATS, 2020)

Doubly infinite residual networks: a diffusion process approach. - Stefano Peluchetti; Stefano Favaro (CoRR, 2020)

A Bayesian nonparametric approach to count-min sketch under power-law data streams. - Emanuele Dolera; Stefano Favaro; Stefano Peluchetti (AISTATS, 2021)

Large-width functional asymptotics for deep Gaussian neural networks. - Daniele Bracale; Stefano Favaro; Sandra Fortini; Stefano Peluchetti (ICLR, 2021)

Infinite-channel deep stable convolutional neural networks. - Daniele Bracale; Stefano Favaro; Sandra Fortini; Stefano Peluchetti (CoRR, 2021)

Deep Stable neural networks: large-width asymptotics and convergence rates. - Stefano Favaro; Sandra Fortini; Stefano Peluchetti (CoRR, 2021)

Consistent estimation of small masses in feature sampling. - Fadhel Ayed; Marco Battiston; Federico Camerlenghi; Stefano Favaro (J. Mach. Learn. Res., 2021)

Doubly infinite residual neural networks: a diffusion process approach. - Stefano Peluchetti; Stefano Favaro (J. Mach. Learn. Res., 2021)

Conformal Frequency Estimation with Sketched Data. - Matteo Sesia; Stefano Favaro (NeurIPS, 2022)

Neural tangent kernel analysis of shallow α-Stable ReLU neural networks. - Stefano Favaro; Sandra Fortini; Stefano Peluchetti (CoRR, 2022)

Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions. - Alberto Bordino; Stefano Favaro; Sandra Fortini (Trans. Mach. Learn. Res., 2022)

Quantitative CLTs in Deep Neural Networks. - Stefano Favaro; Boris Hanin; Domenico Marinucci; Ivan Nourdin; Giovanni Peccati (CoRR, 2023)

Frequency and cardinality recovery from sketched data: a novel approach bridging Bayesian and frequentist views. - Mario Beraha; Stefano Favaro; Matteo Sesia (CoRR, 2023)

Learning-augmented count-min sketches via Bayesian nonparametrics. - Emanuele Dolera; Stefano Favaro; Stefano Peluchetti (J. Mach. Learn. Res., 2023)

Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries. - Matteo Sesia; Stefano Favaro; Edgar Dobriban (J. Mach. Learn. Res., 2023)

Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities. - Alberto Bordino; Stefano Favaro; Sandra Fortini (AABI, 2024)

A Nonparametric Bayes Approach to Online Activity Prediction. - Mario Beraha; Lorenzo Masoero; Stefano Favaro; Thomas S. Richardson 001 (CoRR, 2024)

Improved prediction of future user activity in online A/B testing. - Lorenzo Masoero; Mario Beraha; Thomas S. Richardson 001; Stefano Favaro (CoRR, 2024)

Random measure priors in Bayesian recovery from sketches. - Mario Beraha; Stefano Favaro; Matteo Sesia (J. Mach. Learn. Res., 2024)

Large-width asymptotics and training dynamics of α-Stable ReLU neural networks. - Stefano Favaro; Sandra Fortini; Stefano Peluchetti (Trans. Mach. Learn. Res., 2024)

Function-Space MCMC for Bayesian Wide Neural Networks. - Lucia Pezzetti; Stefano Favaro; Stefano Peluchetti (AISTATS, 2025)

Conformal Classification with New Labels. - Tianmin Xie; Ziyi Liang; Stefano Favaro; Matteo Sesia (COPA, 2025)

Gaussian credible intervals in Bayesian nonparametric estimation of the unseen. - Claudia Contardi; Emanuele Dolera; Stefano Favaro (CoRR, 2025)

Student-t processes as infinite-width limits of posterior Bayesian neural networks. - Francesco Caporali; Stefano Favaro; Dario Trevisan (CoRR, 2025)