Novel Loss

Novel loss functions are being actively developed to improve the performance and robustness of various machine learning models. Research focuses on designing losses tailored to specific challenges, such as mitigating label noise in decision trees, regulating biases in neural network portfolio optimization, and enhancing the efficiency and accuracy of physics-informed neural networks solving partial differential equations. These advancements aim to address limitations in existing loss functions, leading to improved model generalization, stability, and ultimately, more reliable and effective applications across diverse fields.