79 citations · 261 across the 26 of their papers we have counts for
8 papers · 1 filter
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…
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Qinghua Liu, Andrew Henry Reiner, Arnoldo Frigessi +1
Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Curre…
Bayesian Spatial Monotonic Multiple Regression
Christian Rohrbeck, Deborah Costain, Arnoldo Frigessi
We consider monotonic, multiple regression for a set of contiguous regions (lattice data). The regression functions permissibly vary between regions and exhibit geographical struct…
Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
Øystein Sørensen, Arnoldo Frigessi, Magne Thoresen
In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates p exceeds the sample size n, regularized method…
Probabilistic preference learning with the Mallows rank model
Valeria Vitelli, Øystein Sørensen, Marta Crispino +2
Ranking and comparing items is crucial for collecting information about preferences in many areas, from marketing to politics. The Mallows rank model is among the most successful a…