Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
Journal of the american statistical association , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Introduces a margin-adaptive confidence ranking method that learns an estimator from simulated diversity and derives margin-dependent generalization bounds for use in fixed-sequence testing of LLM-human agreement.
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Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
Introduces a margin-adaptive confidence ranking method that learns an estimator from simulated diversity and derives margin-dependent generalization bounds for use in fixed-sequence testing of LLM-human agreement.