Abstract: In this work, we address the problem of sparse regression vector estimation in the presence of corrupted samples, with a particular focus on accurately identifying the support. Traditional methods, such as the Least Absolute Shrinkage and Selection Operator (LASSO), often fail in such scenarios, exhibiting inconsistency. To tackle this challenge, we propose a combinatorial,… Read More »Robust Estimation of a Sparse Linear Model: Provable Guarantees with Non-convexity by Deepak Maurya