Bayesian softmax-gated mixture-of-experts models achieve posterior contraction for density estimation and parameter recovery using Voronoi losses, plus two strategies for choosing the number of experts.
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A hierarchical shrinkage model is introduced for node-parent conditional probabilities in discrete Bayesian networks, enabling posterior sampling and structure learning that handles sparse counts.
BERTweet classifier labels congressional tweets as problem or solution framing with weighted F1 above 0.8 on cross-validation.
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On Bayesian Softmax-Gated Mixture-of-Experts Models
Bayesian softmax-gated mixture-of-experts models achieve posterior contraction for density estimation and parameter recovery using Voronoi losses, plus two strategies for choosing the number of experts.
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Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
A hierarchical shrinkage model is introduced for node-parent conditional probabilities in discrete Bayesian networks, enabling posterior sampling and structure learning that handles sparse counts.
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Classifying Problem and Solution Framing in Congressional Social Media
BERTweet classifier labels congressional tweets as problem or solution framing with weighted F1 above 0.8 on cross-validation.