Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Mixed citations
Journal of the American Statistical Association , year =
Mixed citation behavior. Most common role is background (60%).
31
Pith papers citing it
Background
60% of classified citations
citation-role summary
background 4
method 1
citation-polarity summary
representative citing papers
Introduces TSBM, a new Bayesian model for directed networks that enforces ordered blocks via transitivity-inducing priors on directional imbalance and jointly infers block count with an age-ordered partition prior.
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
A semiparametric Bayesian framework with novel similarity-weighted Bayesian bootstrap for estimating natural direct and indirect effects in cluster randomized trials with limited clusters.
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.
Introduces extended bridge functions and derives identification results for joint interventional distributions retaining proxy variables in proximal causal inference.
CORE-Cox learns low-rank Cox coefficients across outcomes in a source cohort then applies regularized adaptation to a target cohort, yielding C-index gains from 0.733 to 0.766 in UK Biobank and 0.628 to 0.658 in MIMIC-IV Asian subgroups under nested cross-validation.
A learnable continuous perturbation framework for LLM token prefixes via latent vector transformations, optimized through unbiased estimating equations, yields gains in out-of-domain performance.
Establishes identification of the full data law with hidden outcomes via proxies and develops multiply robust influence function estimators for causal effects.
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.
A new two-sample inference method trains a distinguisher on real and classifier-generated data to produce asymptotically valid tests for whether a black-box classifier matches the true conditional distribution.
A penalized likelihood estimator for GEV parameters, weighted by generalized random forest weights, is introduced for extreme quantile regression to improve tail extrapolation and handle many predictors.
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
A new outlier detection method for mixed continuous and ordinal data using latent Gaussian models and robust MCD estimation with a breakdown theorem.
The support function of the identified set for solutions to conditional linear programs is expressed as an average of intersections of regression functions and shown to be a regular parameter admitting standard asymptotic inference.
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
Bayesian ideal point model applied to 341k Debate.org votes finds conviction drives personal freedom topics while conformity drives moral conviction topics including abortion, gun rights, and global warming.
A Neyman-orthogonal estimator for risk heterogeneity between groups is consistent and asymptotically normal, reduces finite-sample bias relative to likelihood methods in simulations, and identifies ethnicity-specific effects in eICU mortality data that standard approaches miss.
Develops SICS and RCRS screening methods for consistent selection of sparse active predictors and change points in high-dimensional structural break predictive regressions that may involve stationary or cointegrated series.
citing papers explorer
-
Proximal Causal Inference for Hidden Outcomes
Establishes identification of the full data law with hidden outcomes via proxies and develops multiply robust influence function estimators for causal effects.