Pith Number

pith:UHKNMZPJ

pith:2021:UHKNMZPJLTUMPHPKBJGERZIKKZ

not attested not anchored not stored refs resolved

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Anastasios N. Angelopoulos, Stephen Bates

Any pre-trained model can be wrapped into prediction sets with guaranteed finite-sample coverage, regardless of how the model was built or what the data distribution is.

arxiv:2107.07511 v6 · 2021-07-15 · cs.LG · cs.AI · math.ST · stat.ME · stat.ML · stat.TH

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Add to your LaTeX paper

\usepackage{pith}
\pithnumber{UHKNMZPJLTUMPHPKBJGERZIKKZ}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Central claim unavailable from malformed reader payload.

C2weakest assumption

Load-bearing assumption unavailable from malformed reader payload.

C3one line summary

Pith review generated a malformed one-line summary.

References

137 extracted · 137 resolved · 1 Pith anchors

[1] V. Vovk, A. Gammerman, and G. Shafer, Algorithmic Learning in a Random World . Springer, 2005 2005

[2] Inductive conﬁdence machines for regression 2002

[3] Distribution-free prediction bands for non-parametric regression 2014

[4] Uncertainty sets for image classiﬁers using conformal prediction 2021

[5] Machine-learning applications of algorithmic random- ness 1999

Formal links

3 machine-checked theorem links

Cited by

113 papers in Pith

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers It

ConRad: Efficient Conformal Prediction for Radiomics

Eigenvalue Calibration for Semantic Embeddings of Large Language Models

Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators

Combining gravitational wave search pipelines to find subthreshold signals in GWTC-5.0

Receipt and verification

First computed	2026-07-05T05:23:06.890987Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a1d4d665e95ce8c79dea0a4c48e50a566969c5081c4d4dc68abc3b169e70766e

Aliases

arxiv: 2107.07511 · arxiv_version: 2107.07511v6 · doi: 10.48550/arxiv.2107.07511 · pith_short_12: UHKNMZPJLTUM · pith_short_16: UHKNMZPJLTUMPHPK · pith_short_8: UHKNMZPJ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UHKNMZPJLTUMPHPKBJGERZIKKZ \
  | 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: a1d4d665e95ce8c79dea0a4c48e50a566969c5081c4d4dc68abc3b169e70766e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2f234154506a01239e49c54d70788fb953d00373b33f482b77e74e665ac04a40",
    "cross_cats_sorted": [
      "cs.AI",
      "math.ST",
      "stat.ME",
      "stat.ML",
      "stat.TH"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2021-07-15T17:59:50Z",
    "title_canon_sha256": "6cf3dff8fc7186ff62e6f3bd5e95c9f6687eb4a83813adc552f1ab8f3cbc215c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2107.07511",
    "kind": "arxiv",
    "version": 6
  }
}