Unsupervised Domain Shift Detection with Interpretable Subspace Attribution
We developed a tool for detecting domain shifts, namely subtle differences in the probability distributions of datasets. We identify these shifts using an algorithm designed to detect localised density anomalies in high-dimensional feature spaces. If an anomaly is present, we then identify the feature subspace in which the anomaly is most pronounced. This allows us to trace the domain shift to a small set of features, making the shift interpretable. Moreover, we provide a protocol for compensating domain shifts by extracting, from two unlabelled datasets, subsets of samples with no detectable residual distributional difference. We validate the framework on controlled 20-dimensional benchm...
| Authors | Sebastian Springer, Alessandro Laio |
|---|---|
| Primary category | stat.ML |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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