Attention in minimal transformers under corruption performs in-context empirical Bayes via a single kernel-weighted posterior mean step followed by depth-driven particle dynamics refinement.
Solving empirical Bayes via transform- ers
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
A Gamma-smoothed NPMLE for Poisson empirical Bayes achieves optimal nearly parametric rates for posterior means and enables asymptotically exact, shorter marginal coverage confidence sets under compact support.
InfoAtlas is a pretrained neural model for zero-shot mutual information estimation that matches state-of-the-art accuracy with 100x speedup and handles varying dimensions via a single model.
Proves frequentist merging of Bayesian (Dirichlet process) and quasi-Bayesian (Newton's algorithm) empirical Bayes estimators for Poisson compound decisions via concentration rates on marginal PMFs and excess risks, with multidimensional extension.
citing papers explorer
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Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics
Attention in minimal transformers under corruption performs in-context empirical Bayes via a single kernel-weighted posterior mean step followed by depth-driven particle dynamics refinement.
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InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate
InfoAtlas is a pretrained neural model for zero-shot mutual information estimation that matches state-of-the-art accuracy with 100x speedup and handles varying dimensions via a single model.