High-dimensional settings, where the data dimension ($d$) far exceeds the number of observations ($n$), are common in many statistical and machine learning applications. Methods based on $\ell_1$-relaxation, such as Lasso, are very popular for sparse recovery in these settings... (read more)

PDF
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.