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.
Compute ˆθ := argmax θ ∈ Rm L(θ), (40) where L(θ) denotes the marginal likelihood, L(θ) := p( ¯β | s, θ, D) = pY j=1 R N ( ¯βj; 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.