Call for papers

Two categories of papers are of interest:

  • papers reporting original, unpublished research
  • papers {published in 2022 / currently under submission / accepted in 2022} at other {workshops / conferences / journals}, provided this double submission does not violate the rules of these {workshops / conferences / journals}

We seek papers on any of the following topics, which will form the main themes of the workshop:

  • Binary, multiclass, and ordinal LQ
  • Supervised algorithms for LQ
  • Semi-supervised / transductive LQ
  • Deep learning for LQ
  • Representation learning for LQ
  • LQ and dataset shift
  • Evaluation measures for LQ
  • Experimental protocols for the evaluation of LQ
  • Quantification of streaming data
  • Quantifying text by topic and quantifying text by sentiment
  • Novel applications of LQ

and other topics of relevance to LQ.

Important dates (all 23:59 AoE)
  • Paper submission deadline: June 20, 2022
  • A/R notification deadline: July 13, 2022
  • Final copy submission deadline: August 30, 2022
  • LQ 2022 workshop: September 23, 2022 (morning)

Papers should be submitted via EasyChair.

Papers should be formatted according to the same format as for the main ECML/PKDD 2022 conference, and should be up to 16 pages (including references) in length; however, this is just the upper bound, and contributions of any length up to this bound will be considered.

Other information

The workshop will be a hybrid event, but it is strongly recommended that authors of accepted papers present the work in-presence. At least one author of each accepted paper must register to present the work. The proceedings of the workshop will not be formally published, so as to allow authors to resubmit their work to other conferences. Informal proceedings will be published on the workshop website; however, for each accepted paper, it will be left at the discretion of the authors to decide whether to contribute their paper or not to these proceedings.



LQ 2022 is a half-day workshop co-located with the ECML/PKDD 2022 conference, and will take place in the morning of Friday, September 23, 2022.

Learning to Quantify (LQ - also known as “quantification“, or “supervised prevalence estimation“, or “class prior estimation“), is the task of training class prevalence estimators via supervised learning. In other words, the task of these trained models is to estimate, given an unlabelled sample of data items and a set of classes, the prevalence (i.e., relative frequency) of each such class in the sample.

LQ is interesting in all applications of classification in which the final goal is not determining which class (or classes) individual unlabelled data items belong to, but estimating the percentages of data items that belong to the classes of interest, i.e., estimating the distribution of the unlabelled data items across the classes. Example disciplines whose interest in labelling data items is at the aggregate level (rather than at the individual level), are the social sciences, political science, market research, ecological modelling, and epidemiology.

While LQ may in principle be solved by classifying each data item in the sample and counting how many such items have been labelled with a certain class, it has been shown that this “classify and count” method yields suboptimal quantification accuracy. As a result, quantification is now no longer considered a mere byproduct of classification, and has evolved as a task of its own.

The goal of this workshop is to bring together all researchers interested in methods, algorithms, evaluation measures, evaluation protocols, and methodologies for LQ, as well as practitioners interested in the practical application of the above to managing large quantities of data.

LQ 2022 is supported by the SoBigData++ project, funded by the European Commission (Grant 871042) under the H2020 Programme INFRAIA-2019-1, and by the AI4Media project, funded by the European Commission (Grant 951911) under the H2020 Programme ICT-48-2020. The organizers’ opinions do not necessarily reflect those of the European Commission.

ai4media logo sobigdata logo


Juan José del Coz

Juan José del Coz

Artificial Intelligence Center, University of Oviedo, Spain

Pablo González

Pablo González

Artificial Intelligence Center, University of Oviedo, Spain

Alejandro Moreo

Alejandro Moreo

Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

Fabrizio Sebastiani

Fabrizio Sebastiani

Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy

Program Committee
  • Rocío Alaíz-Rodríguez, University of León, ES
  • Gustavo Batista, University of New South Wales, AU
  • Mirko Bunse, University of Dortmund, DE
  • Andrea Esuli, Consiglio Nazionale delle Ricerche, IT
  • Alessandro Fabris, Università di Padova, IT
  • Cèsar Ferri, Universitat Politècnica de València, ES
  • George Forman, Amazon Research, US
  • Wei Gao, Singapore Management University, SG
  • Eric Gaussier, University of Grenoble, FR
  • Rafael Izbicki, Federal University of São Carlos, BR
  • André G. Maletzke, Universidade Estadual do Oeste do Paraná, BR
  • Marco Saerens, Catholic University of Louvain, BE
  • Dirk Tasche, Swiss Financial Market Supervisory Authority, CH


LQ 2022 is a half-day workshop, and will take place in Grenoble in the morning of Friday, September 23, 2022. The program of the workshop will feature a keynote talk by Marco Saerens (Université Catholique de Louvain), oral presentations of all the accepted papers, and a final session of discussion.

The following is a preliminary program (precise timings are subject to change).

Friday, September 23

10:3010:40Organizers' Introduction
10:4011:25Keynote talk: Marco Saerens (Université Catholique de Louvain): The old EM algorithm for quantification learning: Some past and recent results [slides] (Chair: Alejandro Moreo)
11:2512:45Regular papers (Chair: Pablo González)
11:2511:45Unification of Algorithms for Quantification and Unfolding, by Mirko Bunse and Katharina Morik (University of Dortmund, DE)
11:4512:05Class Prior Estimation under Covariate Shift: No Problem?, by Dirk Tasche (Independent researcher, CH)
12:0512:25Semi-Automated Estimation of Weighted Rates for E-commerce Catalog Quality Monitoring, by Mauricio Sadinle, Karim Bouyarmane, Grant Galloway, Shioulin Sam, Changhe Yuan and Ismail Tutar (Amazon, US)
12:2512:45On Multi-Class Extensions of Adjusted Classify and Count, by Mirko Bunse (University of Dortmund, DE)
12:4513:30Open discussion (Chair: Juan José del Coz)


The proceedings of LQ 2022 are available here.