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.
Mixed citations
and Banerjee, Sudipto, year = 2005, month = dec
Mixed citation behavior. Most common role is method (60%).
12
Pith papers citing it
Method
60% of classified citations
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method 3
background 2
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years
2026 12representative citing papers
The Paired Swap Permutation Test is an exact non-parametric procedure that compares explanatory power of two dependent predictors via symmetric within-subject swapping for categorical data and ECDF mapping for continuous data.
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
An identification theorem shows that a randomized experiment and simulator together recover causal model values from confounded logs, with logs used only afterward to reduce estimation error.
The ultrametric phylogenetic Laplacian has closed-form eigenvalues that aggregate clade-weighted branch lengths and eigenvectors supported on individual clades, enabling linear-time spectral reconstruction and eigenmode analysis of traits.
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
Spectral analysis of four Bennu sites reveals statistically significant heterogeneity in hydration and silicate features at 2-10 m scales, with Nightingale encompassing the full observed range.
citing papers explorer
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Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
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The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice
An identification theorem shows that a randomized experiment and simulator together recover causal model values from confounded logs, with logs used only afterward to reduce estimation error.
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Quantifying Surface Heterogeneity Across Asteroid (101955) Bennu using Candidate Site Remote Sensing Data
Spectral analysis of four Bennu sites reveals statistically significant heterogeneity in hydration and silicate features at 2-10 m scales, with Nightingale encompassing the full observed range.