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This paper introduces a general regularized thresholded least-square procedure estimating a structured signal $\theta_*\in\mathbb{R}^d$ from the following observations: $y_i = f(\langle\mathbf{x}_i, \theta_*\rangle, \xi_i),~i\in\{1,2,\cdots,N\}$, with i.i.d.
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[author] Hastie, TrevorT., Tibshirani, RobertR. and Wainwright, MartinM. (2015). Statistical learning with sparsity: the lasso and generalizations. CRC press
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[author] Plan, YanivY. and Vershynin, RomanR. (2016). The generalized Lasso with non-linear observations. IEEE Transactions on Information Theory 62 1528–1537
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