Quadratic Loss

Quadratic loss functions are a cornerstone in various machine learning and optimization problems, serving as a measure of error between predicted and actual values. Current research focuses on improving algorithms that minimize quadratic loss, particularly within the contexts of online learning, few-shot learning, and deep learning models like Deep Equilibrium Models. This includes developing novel optimization strategies, such as adaptive learning rate schemes and specialized optimizers to avoid degenerate solutions, and analyzing the convergence properties of these algorithms under different conditions. The advancements in understanding and optimizing quadratic loss have significant implications for improving the efficiency and accuracy of numerous machine learning applications, including drug discovery and reinforcement learning.