Pre-trained TabPFN acts as an effective training-free summary network for neural posterior estimation, matching or outperforming standard methods while preserving useful marginal and location information in the posterior.
Transactions on Machine Learning Research , issn=
2 Pith papers cite this work. Polarity classification is still indexing.
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MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.
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Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation
Pre-trained TabPFN acts as an effective training-free summary network for neural posterior estimation, matching or outperforming standard methods while preserving useful marginal and location information in the posterior.
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MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
MIRA is a new analytic score for conditional distribution accuracy derived from equal probability mass assignment, enabling Bayesian model comparison via direct posterior validation.