Loss Estimation

Loss estimation focuses on accurately quantifying the discrepancy between predicted and actual outcomes across diverse applications, from financial risk assessment to machine learning model training. Current research emphasizes improving loss approximation through advanced machine learning techniques like XGBoost and Physics-Informed Neural Networks (PINNs), as well as optimizing training processes by strategically sampling data points with high approximate losses. These advancements aim to enhance the efficiency and accuracy of various models, leading to improved decision-making in fields ranging from finance and acoustics to natural language processing.