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Prediction-Powered Inference
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Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients, without making any assumptions on the machine-learning algorithm that supplies the predictions. Furthermore, more accurate predictions translate to smaller confidence intervals. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning. The benefits of prediction-powered inference are demonstrated with datasets from proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.
Forward citations
Cited by 3 Pith papers
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Power-Optimal Covariate Adjustment for Switchback Experiments
Training the CUPAC covariate and its regression coefficient with a between-cell-weighted loss improves switchback estimator power, with gains concentrated in within-noise-dominated regimes.
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QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
QuEst gives point estimates and asymptotic confidence intervals for quantile-based distributional measures by optimally combining scarce observed data with abundant model-imputed data.
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Efficient Sequential Evaluation of Large Language Models
A confidence-sequence framework for sequentially estimating an LLM's average benchmark accuracy under adaptive question selection, with growth-oriented sampling rules that in practice often lose to uniform sampling.
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