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.
In practice given that the column of X are centered xt jxt j = n − 1 for al j = 1
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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.