Regression Loss

Regression loss functions, crucial for training models to predict continuous values, are undergoing significant refinement. Current research focuses on developing losses that improve model performance by addressing issues like imbalanced data, handling censored outcomes in survival analysis, and enhancing the interpretability of learned representations. This involves exploring alternatives to standard squared error, such as those based on divergence measures or incorporating pairwise relationships between predictions, ultimately aiming for more accurate, robust, and efficient regression models. These advancements have implications across diverse fields, including generative modeling, medical image analysis, and weather forecasting.