Spectral Regularized Kernel Two-Sample Tests

arXiv:2212.09201v3 Announce Type: replace-cross
Abstract: Over the last decade, an approach that has gained a lot of popularity to tackle nonparametric testing problems on general (i.e., non-Euclidean) domains is based on the notion of reproducing kernel Hilbert space (RKHS) embedding of probability distributions. The main goal of our work is to understand the optimality of two-sample tests constructed based on this approach. First, we show the popular MMD (maximum mean discrepancy) two-sample test to be not optimal in terms of …

abstract arxiv cs.lg domains embedding general kernel math.st non-euclidean notion probability sample space stat.ml stat.th testing tests type work