Abstract Number:
3444
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Tijana Zrnic (1)
Institutions:
(1) University of California, N/A
First Author:
Presenting Author:
Abstract Text:
From proteomics to remote sensing, machine learning predictions are beginning to substitute for real data when collection of the latter is difficult, slow or costly. In this talk I will present recent and ongoing work that permits the use of predictions for the purpose of valid statistical inference. I will discuss the use of machine learning predictions as substitutes for high-quality data on one hand, and as a tool for guiding real data collection on the other. In both cases, machine learning allows for a significant boost in statistical power compared to "classical" baselines for inference that do not leverage prediction.
Keywords:
machine learning|prediction-powered inference|active inference| | |
Sponsors:
IMS
Tracks:
Statistical Methodology
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