kernel regression part1
Working with Kernel Regression part1(Machine Learning)
Abstract: In practice, encoding invariances into models helps sample complexity. In this work, we tighten and generalize theoretical results on how invariances improve sample complexity. In particular, we provide minimax optimal rates for kernel ridge regression on any manifold, with a target function that is invariant to an arbitrary group action on the manifold. Our results hold for (almost) any group action, even groups of positive dimension. For a finite group, the gain increases the "effective" number of samples by the group size.