PUMA uses model averaging to jointly handle uncertainties from model misspecification, tuning, and ML choice, delivering asymptotic in-sample and out-of-sample prediction optimality plus estimation consistency.
A unified framework for semiparametrically efficient semi-supervised learning.arXiv preprint arXiv:2502.17741
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 8verdicts
UNVERDICTED 8representative citing papers
An MOE-powered PPI framework adaptively blends multiple predictors to achieve minimal variance and a best-expert guarantee for semi-supervised mean estimation, linear regression, quantile estimation, and M-estimation, supported by non-asymptotic coverage bounds.
Post-hoc calibration of miscalibrated black-box predictions on a labeled sample improves efficiency of prediction-powered inference for semisupervised mean estimation.
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
Non-asymptotic analysis of prediction-powered mean estimation shows that no-regret learning for query probabilities converges to the maximum allowed constant value, independent of covariates.
A survey synthesizing representative advances, common themes, and open problems in high-dimensional statistics while pointing to key entry-point works.
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.
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Prediction-Powered Linear Regression: A Balance Between Interpretation and Prediction
PUMA uses model averaging to jointly handle uncertainties from model misspecification, tuning, and ML choice, delivering asymptotic in-sample and out-of-sample prediction optimality plus estimation consistency.
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Prediction-powered Inference by Mixture of Experts
An MOE-powered PPI framework adaptively blends multiple predictors to achieve minimal variance and a best-expert guarantee for semi-supervised mean estimation, linear regression, quantile estimation, and M-estimation, supported by non-asymptotic coverage bounds.
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Calibeating Prediction-Powered Inference
Post-hoc calibration of miscalibrated black-box predictions on a labeled sample improves efficiency of prediction-powered inference for semisupervised mean estimation.
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Transporting treatment effects by calibrating large-scale observational outcomes
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.
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A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
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Revisiting Active Sequential Prediction-Powered Mean Estimation
Non-asymptotic analysis of prediction-powered mean estimation shows that no-regret learning for query probabilities converges to the maximum allowed constant value, independent of covariates.
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High-Dimensional Statistics: Reflections on Progress and Open Problems
A survey synthesizing representative advances, common themes, and open problems in high-dimensional statistics while pointing to key entry-point works.
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Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens
A review organizes externally controlled trial methodology through causal estimands and identifiability assumptions for single-arm and hybrid designs with borrowing strategies.