Outlier Robust

Outlier-robust estimation focuses on developing methods for accurately estimating parameters from datasets containing erroneous or atypical data points (outliers). Current research emphasizes developing computationally efficient algorithms, such as Kalman filter variations and robust regression techniques, often incorporating Bayesian or generalized Bayesian frameworks to handle uncertainty and model misspecification. These advancements are crucial for improving the reliability and accuracy of various applications, including state estimation in dynamical systems, machine learning, and geometric perception in robotics and computer vision, where outliers can severely degrade performance. The field is also exploring theoretical guarantees and performance bounds for these methods, particularly in high-dimensional settings.