1 citations · 1 across the 6 of their papers we have counts for
8 papers
Poisson empirical Bayes estimation of sums of random variables via minimum-distance methods
Stefano Favaro, Sandra Fortini, Soham Jana
The estimation of sums of functions of observable and unobservable variables is a long-standing problem in statistics, with applications in many domains. We consider this problem i…
Merging of Bayes and quasi-Bayes empirical Bayes procedures for Poisson compound decisions
Stefano Favaro, Sandra Fortini
The Poisson compound decision problem is a long-standing problem in statistics, in which empirical Bayes methods are used to estimate Poisson means under a mixture model. We study…
Elements of Conformal Prediction
Matteo Sesia, Stefano Favaro
Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution and detailed analyses of how models learn…
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Francesco Caporali, Stefano Favaro, Dario Trevisan
The asymptotic properties of Bayesian Neural Networks (BNNs) have been extensively studied, particularly regarding their approximations by Gaussian processes in the infinite-width…
Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem
Stefano Favaro, Sandra Fortini
The Poisson compound decision problem is a long-standing problem is statistics, for which empirical Bayes methods are commonly used to estimate Poisson means in static or batch set…
Quasi-Bayesian sequential deconvolution
Stefano Favaro, Sandra Fortini
Density deconvolution is the inverse problem of estimating a probability density from observations contaminated by additive noise. Traditionally studied in static or batch settings…
Function-Space MCMC for Bayesian Wide Neural Networks
Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti
Bayesian Neural Networks represent a fascinating confluence of deep learning and probabilistic reasoning, offering a compelling framework for understanding uncertainty in complex p…
Quantitative CLTs in Deep Neural Networks
Stefano Favaro, Boris Hanin, Domenico Marinucci +2
We study the distribution of a fully connected neural network with random Gaussian weights and biases in which the hidden layer widths are proportional to a large constant . Und…