MAM is a bilevel-optimized sparse additive model that auto-learns loss weights via an MLP to handle complex noise, with convergence and variable selection consistency guarantees.
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Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
MAM is a bilevel-optimized sparse additive model that auto-learns loss weights via an MLP to handle complex noise, with convergence and variable selection consistency guarantees.