PPI++: Efficient Prediction-Powered Inference
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909149717069824 |
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| author | Angelopoulos, Anastasios N. Duchi, John C. Zrnic, Tijana |
| author_facet | Angelopoulos, Anastasios N. Duchi, John C. Zrnic, Tijana |
| contents | We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available predictions, yielding easy-to-compute confidence sets -- for parameters of any dimensionality -- that always improve on classical intervals using only the labeled data. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01453 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PPI++: Efficient Prediction-Powered Inference Angelopoulos, Anastasios N. Duchi, John C. Zrnic, Tijana Machine Learning Methodology We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available predictions, yielding easy-to-compute confidence sets -- for parameters of any dimensionality -- that always improve on classical intervals using only the labeled data. 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. |
| title | PPI++: Efficient Prediction-Powered Inference |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2311.01453 |