18 citations · 37 across the 8 of their papers we have counts for
9 papers
Rank-based Bayesian clustering via covariate-informed Mallows mixtures
Emilie Eliseussen, Arnoldo Frigessi, Valeria Vitelli
Data in the form of rankings, ratings, pair comparisons or clicks are frequently collected in diverse fields, from marketing to politics, to understand assessors' individual prefer…
Pseudo-Mallows for Efficient Probabilistic Preference Learning
Qinghua Liu, Valeria Vitelli, Carlo Mannino +2
We propose the Pseudo-Mallows distribution over the set of all permutations of items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has…
Rank-based Bayesian variable selection for genome-wide transcriptomic analyses
Emilie Eliseussen, Thomas Fleischer, Valeria Vitelli
Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data st…
Latent function-on-scalar regression models for observed sequences of binary data: a restricted likelihood approach
Fatemeh Asgari, Mohammad Hossein Alamatsaz, Valeria Vitelli +1
In this paper, we study a functional regression setting where the random response curve is unobserved, and only its dichotomized version observed at a sequence of correlated binary…
A novel framework for joint sparse clustering and alignment of functional data
Valeria Vitelli
We propose a novel framework for sparse functional clustering that also embeds an alignment step. Sparse functional clustering means finding a grouping structure while jointly dete…
BayesMallows: An R Package for the Bayesian Mallows Model
Øystein Sørensen, Marta Crispino, Qinghua Liu +1
BayesMallows is an R package for analyzing data in the form of rankings or preferences with the Mallows rank model, and its finite mixture extension, in a Bayesian probabilistic fr…