Kernel Distance

Kernel distance methods leverage kernel functions to define distances between data points in high-dimensional spaces, often addressing limitations of traditional Euclidean metrics. Current research focuses on developing efficient kernel-based algorithms for diverse applications, including reinforcement learning policy evaluation, causal discovery, and generative modeling of complex data like meshes and high-energy physics events. These advancements improve accuracy and computational efficiency in various fields, enabling better analysis of complex datasets and more robust model evaluation. The resulting improvements in model performance and interpretability have significant implications across scientific disciplines and practical applications.