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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A unified framework for semiparametrically efficient semi-supervised learning.arXiv preprint arXiv:2502.17741
10 Pith papers cite this work. Polarity classification is still indexing.
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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.
Introduces a regularized estimator achieving optimal MSE rates under a new relative balancedness condition while providing safety guarantees that match independent learning when tasks are unrelated.
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
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.
Introduces D2S3 semiparametric framework that extends AIPW estimators to semi-supervised settings with MAR labeling, distribution shift, and decaying overlap, supplying corrected asymptotic rates instead of root-n convergence.
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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Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness and Safety
Introduces a regularized estimator achieving optimal MSE rates under a new relative balancedness condition while providing safety guarantees that match independent learning when tasks are unrelated.
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Transporting treatment effects by calibrating large-scale observational outcomes
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
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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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Semiparametric semi-supervised learning for general targets under distribution shift and decaying overlap
Introduces D2S3 semiparametric framework that extends AIPW estimators to semi-supervised settings with MAR labeling, distribution shift, and decaying overlap, supplying corrected asymptotic rates instead of root-n convergence.
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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.
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