Outlier Sample

Outlier sample analysis focuses on identifying and handling data points deviating significantly from the expected pattern within a dataset. Current research emphasizes robust methods for outlier detection and mitigation across diverse fields, including machine learning model training (e.g., using techniques like pseudo-outlier exposure and diverse outlier sampling), audio analysis (e.g., employing hierarchical linear dynamical systems and multi-frame detection), and signal processing (e.g., adapting lasso-type methods for sparse linear models). These advancements improve the reliability and accuracy of models and algorithms by addressing the challenges posed by outliers, leading to more robust and effective applications in various domains.