Quantification and classification are two supervised learning tasks that, in real applications, are often hampered by changes in data distributions, i.e., by dataset shift. The two tasks differ in what trained models predict: while a classifier predicts the class label of each individual data point, a quantifier predicts the prevalence (i.e., relative frequency) of each class in a set of unlabelled data points. Both classifiers and quantifiers can suffer if dataset shift is at play, at least as long as they are not designed to handle the current type of shift robustly. Research has shown that a quantifier robust to dataset shift can facilitate robust classification, and a classifier robust to dataset shift can facilitate robust quantification.
QCDS 2026, a workshop co-located with the ECML/PKDD 2026 conference, aims to engage the diverse expertise of the ECML/PKDD community; as dataset shift remains a fundamental challenge in real-world deployments, understanding the interplay between classification and quantification is more critical than ever. This workshop provides a collaborative forum for researchers and practitioners to bridge the gap between these two vital fields, to share breakthroughs in machine learning methods robust to dataset shift, and to explore emerging applications.
QCDS 2026 is a follow-up of the Learning to Quantify (LQ) workshop series; while the LQ workshops concentrated exclusively on quantification under dataset shift, QCDS 2026 has a broadened scope, and also encompasses classification under dataset shift and how, when dataset shift is at play, quantification and classification may bring mutual benefit.
QCDS 2026 is supported by project “Future Artificial Intelligence Research” (FAIR) and project “Strengthening the Italian RI for Social Mining and Big Data Analytics” (SoBigData.it), both funded by the European Union under the NextGenerationEU funding scheme (CUP B53D22000980006 and CUP B53C22001760006, respectively) and by the Agency for Science, Business Competitiveness, and Innovation of the Principality of Asturias in Spain (SEKUENS) through the project GRU-GIC-24-018.