A nonlinear adjoint operator rewrites nonlinear least-squares losses as norms of a data-dependent matrix, enabling leverage-score and row-norm sampling with subspace-embedding-style guarantees.
A universal sampling method for reconstructing signals with simple fourier transforms
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Importance Sampling for Nonlinear Models
A nonlinear adjoint operator rewrites nonlinear least-squares losses as norms of a data-dependent matrix, enabling leverage-score and row-norm sampling with subspace-embedding-style guarantees.