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8 Pith papers cite this work. Polarity classification is still indexing.

8 Pith papers citing it

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2026 8

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UNVERDICTED 8

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representative citing papers

On Data Thinning for Model Validation in Small Area Estimation

stat.ME · 2026-04-05 · unverdicted · novelty 7.0 · 2 refs

Data thinning splits area-level observations to enable out-of-sample validation of Fay-Herriot models, with recommendations for thinning parameters that balance bias and variance for stable model comparison.

Learning Perturbations to Extrapolate Your LLM

stat.ML · 2026-05-13 · unverdicted · novelty 6.0

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.

Proximal Causal Inference for Hidden Outcomes

stat.ME · 2026-05-11 · unverdicted · novelty 6.0 · 2 refs

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

cs.LG · 2026-05-10 · unverdicted · novelty 6.0

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

stat.ML · 2026-05-03 · unverdicted · novelty 6.0

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.

Cellwise Outliers

stat.ME · 2026-03-31 · unverdicted · novelty 2.0

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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  • The Statistical Cost of Adaptation in Multi-Source Transfer Learning math.ST · 2026-05-10 · unverdicted · none · ref 20

    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.