List Decodable

List-decodable learning addresses the challenge of estimating parameters from data containing a significant fraction of adversarial outliers, where the goal is to output a short list of candidate solutions, at least one of which is accurate. Current research focuses on developing efficient algorithms for various list-decodable problems, including mean and covariance estimation, regression, and clustering, often employing techniques like spectral methods, sum-of-squares optimization, and iterative filtering. These advancements are crucial for improving the robustness of machine learning models in real-world scenarios where data contamination is prevalent, impacting fields such as robust statistics and distributed learning.