Derives inequalities between L1 density distances and mixing-measure discrepancies to obtain posterior contraction rates for Dirichlet process mixtures with unknown shared scale.
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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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Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Derives inequalities between L1 density distances and mixing-measure discrepancies to obtain posterior contraction rates for Dirichlet process mixtures with unknown shared scale.
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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.