Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
arXiv preprint arXiv:2501.18577 , year=
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UNVERDICTED 6representative citing papers
Empirical Bayes rebiasing learns the bias distribution from paired noisy estimates to produce shorter calibrated intervals than full debiasing while maintaining coverage.
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
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.
citing papers explorer
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Learning U-Statistics with Active Inference
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
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Empirical Bayes Rebiasing
Empirical Bayes rebiasing learns the bias distribution from paired noisy estimates to produce shorter calibrated intervals than full debiasing while maintaining coverage.
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Augmented transfer regression learning for completely missing covariates
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
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Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
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Active Hypothesis Testing under Computational Budgets with Applications to GWAS and LLM
Active hypothesis testing framework uses auxiliary statistics for data-adaptive budget allocation to produce valid p-values or e-values with optimality under independence and admissibility under dependence.
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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.