pith:IDO7Z4NG
MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
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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Claims
MEC attains the semiparametric efficiency bound under weaker assumptions than existing PPI variants.
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.
MEC attains the semiparametric efficiency bound for mean estimation under weaker projection-error conditions than prior PPI methods by using generalized entropy calibration.
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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
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Verify this Pith Number yourself
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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