Combining local-polynomial Lasso variable selection with projected gradient descent yields near-minimax rates for estimating the minimizer and minimum of a sparse nonparametric regression function.
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Optimal minimization of an unknown function in a nonparametric multivariate regression model thanks to a dimension reduction approach
Combining local-polynomial Lasso variable selection with projected gradient descent yields near-minimax rates for estimating the minimizer and minimum of a sparse nonparametric regression function.