Symmetric Noise

Symmetric noise, a type of random error with equal probability of positive and negative deviations, is a crucial area of study across diverse fields, aiming to improve the robustness and accuracy of algorithms in the presence of such noise. Current research focuses on developing efficient algorithms, such as UCB-type methods and approximate message passing (AMP), that effectively handle symmetric noise, particularly in challenging scenarios like heavy-tailed distributions or when the noise structure is unknown. These advancements have significant implications for various applications, including improving the performance of language models and enabling robust signal recovery in challenging estimation problems like tensor and sparse PCA. The development of robust algorithms under symmetric noise models is vital for advancing machine learning, signal processing, and statistical inference.

Papers