Online Realizable Regression and Applications for ReLU Networks
Researchers have developed a new potential method to analyze realizable online regression, a complex problem in machine learning. This method, based on Dudley-type entropy integrals, provides an upper bound for the online dimension, which is crucial for understanding the behavior of such regression tasks. The findings offer a more concrete way to analyze these problems, particularly for ReLU networks, and have implications for both finite and infinite cumulative loss bounds. AI