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pith:JDFY67CT

pith:2023:JDFY67CTDUOKALG35LD5LV42KB

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PPI++: Efficient Prediction-Powered Inference

Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic

PPI++ yields confidence sets for any parameter dimension that always improve on classical intervals by adapting to the quality of machine learning predictions on unlabeled data.

arxiv:2311.01453 v2 · 2023-11-02 · stat.ML · cs.LG · stat.ME

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Claims

C1strongest claim

PPI++ builds on prediction-powered inference (PPI), which targets the same problem setting, improving its computational and statistical efficiency. Real and synthetic experiments demonstrate the benefits of the proposed adaptations.

C2weakest assumption

The method can automatically adapt to the quality of available predictions in a way that guarantees improvement over classical intervals for parameters of any dimensionality.

C3one line summary

PPI++ yields easy-to-compute confidence sets for any-dimensional parameters that always improve on classical intervals from labeled data alone by leveraging abundant ML predictions.

References

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[1] Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I 2023

[2] A. N. Angelopoulos, J. C. Duchi, and T. Zrnic. A note on statistical efficiency in Prediction-Powered Inference. 2023. URL https://web.stanford.edu/~jduchi/projects/ AngelopoulosDuZr23w.pdf 2023

[3] P. Bickel, C. A. J. Klaassen, Y. Ritov, and J. Wellner. Efficient and Adaptive Estimation for Semiparametric Models. Springer Verlag, 1998 1998

[4] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. 18 2004

[5] L. D. Brown. Fundamentals of Statistical Exponential Families . Institute of Mathematical Statistics, Hayward, California, 1986 1986

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Cited by

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Receipt and verification

First computed	2026-05-17T23:38:14.123936Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

48cb8f7c531d1ca02cdbeac7d5d79a505ef314e61378b8116b7a81fe2b42ce67

Aliases

arxiv: 2311.01453 · arxiv_version: 2311.01453v2 · doi: 10.48550/arxiv.2311.01453 · pith_short_12: JDFY67CTDUOK · pith_short_16: JDFY67CTDUOKALG3 · pith_short_8: JDFY67CT

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/JDFY67CTDUOKALG35LD5LV42KB \
  | 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: 48cb8f7c531d1ca02cdbeac7d5d79a505ef314e61378b8116b7a81fe2b42ce67

Canonical record JSON

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