{"paper":{"title":"Unsupervised Domain Shift Detection with Interpretable Subspace Attribution","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Domain shifts appear as localized density anomalies that can be attributed to small sets of features without using labels.","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alessandro Laio, Sebastian Springer","submitted_at":"2026-05-15T12:58:00Z","abstract_excerpt":"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 "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The framework recovers both broad and localized shifts together with their supporting feature subspaces on controlled 20-dimensional benchmarks and, when applied to ECG recordings differing in measurement-device composition, detects device-induced shifts and identifies associated ECG features.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The method assumes that domain shifts manifest as detectable localized density anomalies in the chosen feature representation; if the shift is diffuse or only visible after nonlinear transformations not captured by the subspace search, the detection and attribution steps may miss it.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"An unsupervised method detects domain shifts via localized density anomaly search in feature space, attributes the shift to a minimal subspace, and extracts balanced subsets from two unlabeled datasets.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Domain shifts appear as localized density anomalies that can be attributed to small sets of features without using labels.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ff8168f6d595da9945f059094ce9646a3669743f8173da65ece976e5edbe1fc9"},"source":{"id":"2605.15920","kind":"arxiv","version":1},"verdict":{"id":"5d03e2fc-215e-4cae-983d-724e554c2fad","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:22:20.108723Z","strongest_claim":"The framework recovers both broad and localized shifts together with their supporting feature subspaces on controlled 20-dimensional benchmarks and, when applied to ECG recordings differing in measurement-device composition, detects device-induced shifts and identifies associated ECG features.","one_line_summary":"An unsupervised method detects domain shifts via localized density anomaly search in feature space, attributes the shift to a minimal subspace, and extracts balanced subsets from two unlabeled datasets.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The method assumes that domain shifts manifest as detectable localized density anomalies in the chosen feature representation; if the shift is diffuse or only visible after nonlinear transformations not captured by the subspace search, the detection and attribution steps may miss it.","pith_extraction_headline":"Domain shifts appear as localized density anomalies that can be attributed to small sets of features without using labels."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15920/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T19:31:19.058815Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:31:08.516148Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:46.552547Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.752438Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ba89d7b20ac881a20889b0927c4ea2b68c4f8db93f8cec5f60d8c7ec0cca8918"},"references":{"count":16,"sample":[{"doi":"","year":null,"title":"Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence, editors. Dataset Shift in Machine Learning. Neural Information Processing Series. MIT Press, Cambridge, MA,","work_id":"89162bda-c435-4d36-9e9c-748c8b861021","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.7551/mitpress/9780262017091.001.0001","year":2012,"title":"Vision: A Computational Investigation into the Human Representation and Processing of Visual Information","work_id":"f90bc3f0-7c2b-44f6-a8aa-1420c3a84682","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Stephan Rabanser, Stephan Günnemann, and Zachary C. Lipton. Failing loudly: An empirical study of methods for detecting dataset shift. 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