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pith:2026:IDO7Z4NGNLT3FXMZFHIJFIZAAS
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MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation

Jae Kwang Kim, Se Yoon Lee

Machine-learning-assisted generalized entropy calibration attains the semiparametric efficiency bound for semi-supervised mean estimation under weaker assumptions than prior PPI variants.

arxiv:2604.05446 v2 · 2026-04-07 · stat.ML · cs.LG

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4 Citations open
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Claims

C1strongest claim

MEC attains the semiparametric efficiency bound under weaker assumptions than existing PPI variants.

C2weakest assumption

The calibration framework based on Bregman projections produces weights that align labeled samples with the target population and that the weaker projection-error conditions suffice for validity and efficiency.

C3one line summary

MEC attains the semiparametric efficiency bound for mean estimation under weaker projection-error conditions than prior PPI methods by using generalized entropy calibration.

Formal links

2 machine-checked theorem links

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1 paper in Pith

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First computed 2026-05-29T01:05:08.743108Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

40ddfcf1a66ae7b2dd9929d092a320048f8641f30d54e12a3645d06faf94eed5

Aliases

arxiv: 2604.05446 · arxiv_version: 2604.05446v2 · doi: 10.48550/arxiv.2604.05446 · pith_short_12: IDO7Z4NGNLT3 · pith_short_16: IDO7Z4NGNLT3FXMZ · pith_short_8: IDO7Z4NG
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/IDO7Z4NGNLT3FXMZFHIJFIZAAS \
  | 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: 40ddfcf1a66ae7b2dd9929d092a320048f8641f30d54e12a3645d06faf94eed5
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-04-07T05:30:11Z",
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