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 Royal Statistical Society Series B: Statistical Methodology , volume=
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A general Bayesian framework encodes explicit confidence in uncertainty sources to enable new regularization control and sparsity induction in statistical models.
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
PAC-Bayes bounds for Gibbs posteriors are obtained via singular learning theory, producing explicit and tighter posterior-averaged risk bounds that adapt to data structure in overparameterized models.
New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
A consistency-regularized Euclidean-Wasserstein-2 gradient flow performs joint posterior sampling and prompt optimization in latent space for efficient low-NFE inverse problem solving with diffusion models.
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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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Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation
A general Bayesian framework encodes explicit confidence in uncertainty sources to enable new regularization control and sparsity induction in statistical models.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
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PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory
PAC-Bayes bounds for Gibbs posteriors are obtained via singular learning theory, producing explicit and tighter posterior-averaged risk bounds that adapt to data structure in overparameterized models.
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Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.
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Robust Simulation Based Inference Through Robust Optimal Transport
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
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Consistency Regularised Gradient Flows for Inverse Problems
A consistency-regularized Euclidean-Wasserstein-2 gradient flow performs joint posterior sampling and prompt optimization in latent space for efficient low-NFE inverse problem solving with diffusion models.