Riesz Representer

Riesz representers are mathematical objects used to represent linear functionals in reproducing kernel Hilbert spaces, finding applications in diverse fields like machine learning and signal processing. Current research focuses on leveraging Riesz representers within neural network architectures, particularly for tasks requiring scale invariance or robust estimation in high-dimensional spaces, with methods including Riesz networks and algorithms based on Riesz projections. This work is significant because it enables the development of more efficient and accurate algorithms for problems ranging from image classification and density estimation to causal inference and minimax optimization, improving the performance and robustness of machine learning models.