Kernel Flow

Kernel flow methods aim to learn optimal kernel functions for various machine learning tasks, improving model performance and efficiency by adapting kernels to specific datasets. Current research focuses on applying kernel flows to diverse problems, including scene flow estimation, regression (e.g., using kernel partial least squares), and dynamical systems modeling, often employing gradient-based optimization or cross-validation techniques to find optimal kernel parameters. These advancements offer improved accuracy and efficiency in various applications, such as robotics, autonomous driving, and chemometrics, by enabling more effective non-linear modeling and data-driven kernel design.