1 citations · 1 across the 7 of their papers we have counts for
8 papers
Quantifying Feature Importance for Online Content Moderation
Benedetta Tessa, Alejandro Moreo, Stefano Cresci +2
Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear und…
Transductive Model Selection under Prior Probability Shift
Lorenzo Volpi, Alejandro Moreo, Fabrizio Sebastiani
Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are…
Quantifying Query Fairness Under Unawareness
Thomas Jaenich, Alejandro Moreo, Alessandro Fabris +4
Traditional ranking algorithms are designed to retrieve the most relevant items for a user's query, but they often inherit biases from data that can unfairly disadvantage vulnerabl…
Efficient quantification on large-scale networks
Alessio Micheli, Alejandro Moreo, Marco Podda +3
Network quantification (NQ) is the problem of estimating the proportions of nodes belonging to each class in subsets of unlabelled graph nodes. When prior probability shift is at p…
The \textit{Questio de aqua et terra}: A Computational Authorship Verification Study
Martina Leocata, Alejandro Moreo, Fabrizio Sebastiani
The Questio de aqua et terra is a cosmological treatise traditionally attributed to Dante Alighieri. However, the authenticity of this text is controversial, due to discrepancies w…
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective
Mattia Setzu, Silvia Corbara, Anna Monreale +2
While a substantial amount of work has recently been devoted to enhance the performance of computational Authorship Identification (AId) systems, little to no attention has been pa…
Regularization-Based Methods for Ordinal Quantification
Mirko Bunse, Alejandro Moreo, Fabrizio Sebastiani +1
Quantification, i.e., the task of training predictors of the class prevalence values in sets of unlabeled data items, has received increased attention in recent years. However, mos…
Binary Quantification and Dataset Shift: An Experimental Investigation
Pablo González, Alejandro Moreo, Fabrizio Sebastiani
Quantification is the supervised learning task that consists of training predictors of the class prevalence values of sets of unlabelled data, and is of special interest when the l…