153 citations · 351 across the 22 of their papers we have counts for
3 papers · 1 filter
LeQua@CLEF2022: Learning to Quantify
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of in…
Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach
Alessandro Fabris, Andrea Esuli, Alejandro Moreo +1
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 mus…
QuaPy: A Python-Based Framework for Quantification
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani
QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via…