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Found 3 papers, 1 papers with code
Risk Bounds for Mixture Density Estimation on Compact Domains via the $h$-Lifted Kullback--Leibler Divergence
We consider the problem of estimating probability density functions based on sample data, using a finite mixture of densities from some component class.
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models
This model selection criterion allows us to handle the challenging problem of inferring the number of mixture components, the degree of polynomial mean functions, and the hidden block-diagonal structures of the covariance matrices, which reduces the number of parameters to be estimated and leads to a trade-off between complexity and sparsity in the model.
A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models
Mixture of experts (MoE) are a popular class of statistical and machine learning models that have gained attention over the years due to their flexibility and efficiency.
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