Frouros: an open-source Python library for drift detection in machine learning systems.
A flexible and powerful Python library for dataset shift analysis and characterization, providing supervised and unsupervised evaluation of temporal and multi-source data shifts, visualization tools, and statistical insights for data integrity and model performance monitoring
Code for the paper "Where are we with calibration under dataset shift in image classification?"
statistical tests for drift detection and dataset shift
Official code, results, and reproducibility package for CRIT-AID: reliability auditing for AI decision support under distribution shift, target-definition change, calibration, selective prediction, and conformal uncertainty. Computers 15(9), 560 (2026). DOI: 10.3390/computers15090560
Shift type, not magnitude, determines ML failure modes under deployment shift — a cross-domain audit protocol and benchmark
WaX: explainable Wasserstein distances. Attributes a Wasserstein distance to instances, features and subspaces (Naumann, Kauffmann and Montavon, TPAMI 2026).
A benchmark postmortem on synthetic shortcuts, near-duplicates, and distribution shift. 2nd place at Purple Hack 2026.
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