Robust Fitting

Robust fitting aims to estimate model parameters from data containing outliers, a crucial task across numerous scientific fields. Current research focuses on improving efficiency and accuracy through diverse approaches, including quantum computing algorithms, machine learning models (like CatBoost and neural networks), and novel sketching techniques for high-dimensional data. These advancements enhance the reliability and interpretability of model fitting, particularly in applications like computer vision, astronomy (spectral energy distribution modeling), and geometric regression, leading to more robust and accurate analyses.