Nash integrates neural networks into variational empirical Bayes to learn per-covariate penalties for sparse high-dimensional regression, claiming major speedups and better accuracy on real data.
[2024]), so the and so q∗ βj = maxF (qbj) is given by computing the posterior of the following simple model ¯rj =xjβj + ε (33) βj ∼N (¯bj, σ2
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Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression
Nash integrates neural networks into variational empirical Bayes to learn per-covariate penalties for sparse high-dimensional regression, claiming major speedups and better accuracy on real data.