QCDS2026

Welcome

“Quantification and Classification under Dataset Shift” (QCDS) is a workshop event co-located with the ECML/PKDD 2026 conference. QCDS is a follow-up of the Learning to Quantify (LQ) workshop series, and broadens the focus from quantification to the more general theme of classification under dataset shift.

Quantification and classification are two supervised learning tasks that are both hampered by changes in data distributions, so-called dataset shifts. The two tasks differ in what they learn to predict: while classification predicts the class label of each individual data point, quantification deals with the prediction of the prevalence (i.e., the relative frequency) of each class in every unlabelled set of data. Predicting this prevalence is worthwhile whenever it can change from set to set, due to various types of dataset shift that may exist between these sets and the training data. Both classifiers and quantifiers can suffer from these shifts, at least as long as they are not designed to handle the current type of shift robustly. Notably, a robust quantifier can facilitate robust classification and a robust classifier can facilitate robust quantification under dataset shift.

By broadening our scope, QCDS 2026 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 share breakthroughs in robust methodology, explore emerging applications, and bridge the gap between these two vital fields.

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), all 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.

Call for papers for the QCDS 2026 Workshop

We seek papers on any of the following topics, which will form the main themes of the QCDS 2026 workshop:

Binary, multiclass, multilabel, and ordinal QCDS
Semi-supervised / transductive QCDS
Multi-instance learning / learning for set-structured data
Deep learning for QCDS
Characterizations of dataset shift, and their impact on QCDS
Detecting and measuring (different types of) dataset shift
Evaluation measures and protocols for QCDS
Improving classification under dataset shift via quantification
Classifier calibration under dataset shift
New datasets and applications for evaluating QCDS

and other topics of relevance to QCDS. Two categories of papers are of interest:

papers reporting original, unpublished research;
papers {published in 2026 / currently under submission / accepted in 2026} at other {workshops / conferences / journals}, provided this double submission does not violate the rules of these {workshops / conferences / journals}.

Submission

Papers should be submitted (specifying which of the two above categories they belong to) via CMT.

Papers should be formatted according to Springer’s LNCS template, 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

Important: By submitting a paper the authors commit, in case of acceptance, to have one of them register (according to the rules set by the ECML/PKDD 2026 organizers) and present the paper at the workshop. 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.