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Given the definition ofϕ, we haveϕ◦L 0(x, s(x)) =L 0(x, s(x)) givenM≥r(b+1)≥ ∥x J ∥∞

ForL 0 :R p+1 →R r+|J|+1 , let L0(x, s(x)) = (p−1x⊤W,x ⊤eΘ, s(x))⊤ = (ef ⊤,x ⊤ J , s(x))⊤, eΘij =1{i≤ |J|, j=l i}

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SMART Fine-tuning Factor Augmented Neural Lasso

stat.ML · 2026-04-14 · unverdicted · novelty 5.0 · 2 refs

SMART-FAN-Lasso achieves minimax-optimal excess risk bounds for fine-tuning in high-dimensional nonparametric regression with variable selection, yielding statistical acceleration over single-task learning under conditions on relative sample sizes and function complexities.

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  • SMART Fine-tuning Factor Augmented Neural Lasso stat.ML · 2026-04-14 · unverdicted · none · ref 1 · 2 links

    SMART-FAN-Lasso achieves minimax-optimal excess risk bounds for fine-tuning in high-dimensional nonparametric regression with variable selection, yielding statistical acceleration over single-task learning under conditions on relative sample sizes and function complexities.