3 citations · 6 across the 5 of their papers we have counts for
5 papers
Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets
Luca Benedetti, Eric Boniardi, Leonardo Chiani +4
After being trained on a fully-labeled training set, where the observations are grouped into a certain number of known classes, novelty detection methods aim to classify the instan…
On the intrinsic dimensionality of Covid-19 data: a global perspective
Abhishek Varghese, Edgar Santos-Fernandez, Francesco Denti +2
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covi…
Distributional Results for Model-Based Intrinsic Dimension Estimators
Francesco Denti, Diego Doimo, Alessandro Laio +1
Modern datasets are characterized by a large number of features that may conceal complex dependency structures. To deal with this type of data, dimensionality reduction techniques…
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
Francesco Denti, Federico Camerlenghi, Michele Guindani +1
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In p…
The role of intrinsic dimension in high-resolution player tracking data -- Insights in basketball
Edgar Santos-Fernandez, Francesco Denti, Kerrie Mengersen +1
A new range of statistical analysis has emerged in sports after the introduction of the high-resolution player tracking technology, specifically in basketball. However, this high d…