Proceedings of the 1st International Workshop on Quantification and Classification under Dataset Shift (QCDS 2026)

The 1st International Workshop on Quantification and Classification under Dataset Shift (QCDS 2026 – https://qcds-2026.github.io/) has been held in Napoli, IT, on September 7, 2026, as a satellite workshop of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2026). 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 di!er 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 su!er 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.