Pith Number

pith:VB6XASAC

pith:2026:VB6XASACSKJGFO4ZI3FWZWVGYR

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Unsupervised Domain Shift Detection with Interpretable Subspace Attribution

Alessandro Laio, Sebastian Springer

Domain shifts appear as localized density anomalies that can be attributed to small sets of features without using labels.

arxiv:2605.15920 v1 · 2026-05-15 · stat.ML · cs.LG

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\usepackage{pith}
\pithnumber{VB6XASACSKJGFO4ZI3FWZWVGYR}

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Claims

C1strongest 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.

C2weakest 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.

C3one 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.

References

16 extracted · 16 resolved · 1 Pith anchors

[1] 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,

[2] Vision: A Computational Investigation into the Human Representation and Processing of Visual Information 2012 · doi:10.7551/mitpress/9780262017091.001.0001

[3] Stephan Rabanser, Stephan Günnemann, and Zachary C. Lipton. Failing loudly: An empirical study of methods for detecting dataset shift. InAdvances in Neural Information Processing Systems, vol- ume 32, 2019

[4] Vision: A Computational Investigation into the Human Representation and Processing of Visual Information 2009 · doi:10.7551/mitpress/9780262170055.003.0008

[5] Direct importance estimation for covariate shift adaptation.Annals of the Institute of Statistical Mathematics, 60(4):699–746, December 2008 2008 · doi:10.1007/s10463-008-0197-x

Receipt and verification

First computed	2026-05-20T00:01:45.225629Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a87d704802929262bb9946cb6cdaa6c442cc5d43c66e851dae06d861fc6247ed

Aliases

arxiv: 2605.15920 · arxiv_version: 2605.15920v1 · doi: 10.48550/arxiv.2605.15920 · pith_short_12: VB6XASACSKJG · pith_short_16: VB6XASACSKJGFO4Z · pith_short_8: VB6XASAC

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/VB6XASACSKJGFO4ZI3FWZWVGYR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a87d704802929262bb9946cb6cdaa6c442cc5d43c66e851dae06d861fc6247ed

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "8a8e3aad7051cf512c3f7ef9aef79f96dd25224798872401f446db8278fc5b07",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-15T12:58:00Z",
    "title_canon_sha256": "dbeb338d31ed416c305814d11aeeaddb2d9a3d6d187c4c537766e8c042a5df0a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15920",
    "kind": "arxiv",
    "version": 1
  }
}