Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
Dennis Cook and Rodney Croos-dabrera
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
An R package was created to consolidate and tune Weibull-based tests for pharmacovigilance signal detection using temporal health record data.
citing papers explorer
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.
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ShrinkageTrees: An R Package for Bayesian Tree Ensembles for Survival Analysis and Causal Inference
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.
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Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
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Applying the Weibull Shape Parameter test for signal detection in pharmacovigilance using the R package WSPsignal
An R package was created to consolidate and tune Weibull-based tests for pharmacovigilance signal detection using temporal health record data.