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.
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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
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
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.
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Ordering Stochastic Block Models via prior transitivity
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.
-
Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
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.
-
Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference
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.
-
Decision-Aware Proximal Bridge Learning for Optimal Treatment Selection
Introduces decision-aware proximal bridge learning using a weighted loss and regret bound to enhance optimal treatment selection in settings with hidden confounding.
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To select or not to select: predictively consistent priors instead of model selection
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.
-
Semiparametric Bayesian inference for causal mediation in cluster randomized trials
A semiparametric Bayesian framework with novel similarity-weighted Bayesian bootstrap for estimating natural direct and indirect effects in cluster randomized trials with limited clusters.
-
Tests for Independence of High-Dimensional Nonstationary Time Series
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.
-
Optimized Labeling Resource Allocation for Prediction-Assisted Inference via OPAL
OPAL learns optimal smooth labeling policies from ML uncertainty scores to enable low-variance prediction-assisted inference with finite-sample coverage guarantees.
-
Identifying Interventional Joint Distributions via Extended Bridge Functions
Introduces extended bridge functions and derives identification results for joint interventional distributions retaining proxy variables in proximal causal inference.
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Structured Transfer Learning for Survival Risk Stratification in Data-Sparse Clinical Cohorts
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.
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Learning Perturbations to Extrapolate Your LLM
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.
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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.
-
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
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
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.
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Evaluating Black-Box Classifiers via Stable Adaptive Two-Sample Inference
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.
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Penalized estimation of GEV parameters for extreme quantile regression
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.
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Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Data
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
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A unified approach to outlier identification for mixed-type data
A new outlier detection method for mixed continuous and ordinal data using latent Gaussian models and robust MCD estimation with a breakdown theorem.
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Adaptive Estimation of Aggregated Values of Conditional Linear Programs
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.
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Constrained Weighted Bayesian Bootstrap
Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.
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Disentangling conviction and conformity: a Bayesian ideal point model of voting behaviour in online debates
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.
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Robust inference for risk heterogeneity under group imbalance
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.
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Feature Screening for High-Dimensional Structural Break Predictive Regression
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.
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A Counting Process View of Relational Event Models: Practical Asymptotics
Reviews asymptotic normality conditions for counting-process REMs under varying limits of n and T, with simulations illustrating effects of modeling choices like windowing and log transforms on Cox-type models.
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A Guide to Higher-Order Homophily
A survey of existing measures and models for quantifying and generating higher-order homophily and heterophily in hypergraphs.
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Cellwise Outliers
Cellwise outliers can contaminate over half the cases even at low proportions, necessitating specialized robust techniques for location, covariance, regression, PCA, and tensor data that differ from casewise approaches.
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Comparing Two Proxy Methods for Causal Identification
Compares bridge equation and array decomposition proxy methods for causal identification, highlighting differences in model restrictions and scope of applicability.
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Robust discriminant analysis
A review paper that identifies the outlier sensitivity of classical discriminant analysis and summarizes robust versions based on resistant location and scatter estimators plus diagnostic graphics.
- Sensitivity analysis for causal mediation: bridge score, sharp sensitivity bounds, and calibration
- On Data Thinning for Model Validation in Small Area Estimation