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Found 2 papers, 2 papers with code
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates.
Model selection with lasso-zero: adding straw to the haystack to better find needles
The high-dimensional linear model $y = X \beta^0 + \epsilon$ is considered and the focus is put on the problem of recovering the support $S^0$ of the sparse vector $\beta^0.$ We introduce Lasso-Zero, a new $\ell_1$-based estimator whose novelty resides in an "overfit, then threshold" paradigm and the use of noise dictionaries concatenated to $X$ for overfitting the response.
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