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We consider the problem of recovering a vector $\beta_o \in \mathbb{R}^p$ from $n$ random and noisy linear observations $y= X\beta_o + w$, where $X$ is the measurement matrix and $w$ is noise.
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Parameterless optimal approximate message passing
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Leave-one-out cross-validation is risk consistent for lasso
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