arXiv subjects
Publications and source records attributed to Raphael Arkady Meyer.
In this note, we prove a non-asymptotic version of a theorem by Rácz and Richey, showing that a Wishart matrix is close in total variation to an affine transformation of a GOE matrix. The proof mirrors a proof in a paper by Bubeck, Ding, Eldan, and Rácz, with some changes made to make it non-asymptotic.
We propose a novel combination of optimization tools with learning theory bounds in order to analyze the sample complexity of optimal kernel sum classifiers. This contrasts the typical learning theoretic results which hold for all (potentially suboptimal) classifiers. Our work also justifies assumptions made in prior work on multiple kernel learning. As a byproduct of our analysis, we also provide a new form of Rademacher complexity for hypothesis classes containing only optimal classifiers.