PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
Simpson , author H
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A spatio-temporal disaggregation method that replaces lognormal polygon effects with gamma overdispersion to obtain a marginal negative binomial likelihood, reducing latent variables and enabling fast inference via the Extended Latent Gaussian Model framework.
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
citing papers explorer
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Spatio-Temporal Disaggregation with Changing Areal Boundaries
A spatio-temporal disaggregation method that replaces lognormal polygon effects with gamma overdispersion to obtain a marginal negative binomial likelihood, reducing latent variables and enabling fast inference via the Extended Latent Gaussian Model framework.
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To select or not to select: predictively consistent priors instead of model selection
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
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INLA-RF: A Hybrid Modeling Strategy for Spatio-Temporal Environmental Data
A hybrid INLA-RF framework integrates Bayesian spatio-temporal modeling with random forests through two iterative algorithms to improve predictions and uncertainty quantification for environmental data.
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Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.