Fabrizio Sebastiani
YOU?
Author Swipe
View article: Efficient quantification on large-scale networks
Efficient quantification on large-scale networks Open
View article: Proceedings of the 5th International Workshop on Learning to Quantify (LQ 2025)
Proceedings of the 5th International Workshop on Learning to Quantify (LQ 2025) Open
International audience
View article: Quantifying Query Fairness Under Unawareness
Quantifying Query Fairness Under Unawareness Open
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 vulnerable groups. Fairness in information access systems (IAS) is t…
View article: Efficient quantification on large-scale networks
Efficient quantification on large-scale networks Open
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 play, this task cannot be effectively addressed by first cla…
View article: The \textit{Questio de aqua et terra}: A Computational Authorship Verification Study
The \textit{Questio de aqua et terra}: A Computational Authorship Verification Study Open
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 with Dante's established works and to the absence of contemp…
View article: A Noise-Oriented and Redundancy-Aware Instance Selection Framework
A Noise-Oriented and Redundancy-Aware Instance Selection Framework Open
Fine-tuning transformer-based deep-learning models are currently at the forefront of natural language processing (NLP) and information retrieval (IR) tasks. However, fine-tuning these transformers for specific tasks, especially when dealin…
View article: Proceedings of the4th International Workshop onLearning to Quantify(LQ 2024)
Proceedings of the4th International Workshop onLearning to Quantify(LQ 2024) Open
International audience
View article: Regularization-based methods for ordinal quantification
Regularization-based methods for ordinal quantification Open
Quantification, i.e., the task of predicting the class prevalence values in bags of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms for…
View article: Explainable Authorship Identification in Cultural Heritage Applications
Explainable Authorship Identification in Cultural Heritage Applications Open
While a substantial amount of work has recently been devoted to improving the accuracy of computational Authorship Identification (AId) systems for textual data, little to no attention has been paid to endowing AId systems with the ability…
View article: Binary quantification and dataset shift: an experimental investigation
Binary quantification and dataset shift: an experimental investigation Open
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 labelled data on which the predictor has been trained and th…
View article: AIMH Research Activities 2023
AIMH Research Activities 2023 Open
The AIMH (Artificial Intelligence for Media and Humanities) laboratory is dedicated to exploring and pushing the boundaries in the field of Artificial Intelligence, with a particular focus on its application in digital media and humanities…
View article: Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective Open
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 paid to endowing AId systems with the ability to explain the …
View article: Regularization-Based Methods for Ordinal Quantification
Regularization-Based Methods for Ordinal Quantification Open
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, most quantification research has concentrated on developing al…
View article: Binary Quantification and Dataset Shift: An Experimental Investigation
Binary Quantification and Dataset Shift: An Experimental Investigation Open
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 labelled data on which the predictor has been trained and th…
View article: Product Reviews for Ordinal Quantification
Product Reviews for Ordinal Quantification Open
This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in…
View article: Product Reviews for Ordinal Quantification
Product Reviews for Ordinal Quantification Open
This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in…
View article: Proceedings of the 3rd International Workshop on Learning to Quantify (LQ 2023)
Proceedings of the 3rd International Workshop on Learning to Quantify (LQ 2023) Open
The 3rd International Workshop on Learning to Quantify (LQ 2023 – https: //lq-2023.github.io/) was held in Torino, IT, on September 18, 2023, as a satellite workshop of the European Conference on Machine Learning and Principles and Practic…
View article: Product Reviews for Ordinal Quantification
Product Reviews for Ordinal Quantification Open
This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in…
View article: UCI and OpenML Data Sets for Ordinal Quantification
UCI and OpenML Data Sets for Ordinal Quantification Open
These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, y…
View article: UCI and OpenML Data Sets for Ordinal Quantification
UCI and OpenML Data Sets for Ordinal Quantification Open
These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, y…
View article: Cherenkov Telescope Data for Ordinal Quantification
Cherenkov Telescope Data for Ordinal Quantification Open
This labeled data set is targeted at ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, you…
View article: Cherenkov Telescope Data for Ordinal Quantification
Cherenkov Telescope Data for Ordinal Quantification Open
This labeled data set is targeted at ordinal quantification. The goal of quantification is not to predict the class label of each individual instance, but the distribution of labels in unlabeled sets of data. With the scripts provided, you…
View article: Same or Different? Diff-Vectors for Authorship Analysis
Same or Different? Diff-Vectors for Authorship Analysis Open
In this article, we investigate the effects on authorship identification tasks (including authorship verification, closed-set authorship attribution, and closed-set and open-set same-author verification) of a fundamental shift in how to co…
View article: Multi-Label Quantification
Multi-Label Quantification Open
Quantification, variously called supervised prevalence estimation or learning to quantify , is the supervised learning task of generating predictors of the relative frequencies (a.k.a. prevalence values ) of the classes of interest in unla…
View article: Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach
Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach Open
Algorithms and models are increasingly deployed to inform decisions about people, inevitably affecting their lives. As a consequence, those in charge of developing these models must carefully evaluate their impact on different groups of pe…
View article: Unravelling interlanguage facts via explainable machine learning
Unravelling interlanguage facts via explainable machine learning Open
Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the per…
View article: Improved risk minimization algorithms for technology-assisted review
Improved risk minimization algorithms for technology-assisted review Open
MINECORE is a recently proposed decision-theoretic algorithm for technology-assisted review that attempts to minimise the expected costs of review for responsiveness and privilege in e-discovery. In MINECORE, two probabilistic classifiers …
View article: Same or Different? Diff-Vectors for Authorship Analysis
Same or Different? Diff-Vectors for Authorship Analysis Open
We investigate the effects on authorship identification tasks of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supervised learner. In ``classic'' authorship analysis a featur…
View article: Advanced Topics
Advanced Topics Open
In this chapter we look at a number of “advanced” (or niche) topics in quantification, including quantification for ordinal data, “regression quantification” (the task that stands to regression as “standard” quantification stands to classi…
View article: Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach
Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach Open
Algorithms and models are increasingly deployed to inform decisions about people, inevitably affecting their lives. As a consequence, those in charge of developing these models must carefully evaluate their impact on different groups of pe…