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排序与 U 统计量的经验最小化AI 解读

The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous statistical framework. The goal is to learn a ranking rule for deciding, among two instances, which one is “better,” with minimum ranking risk. Since the natural estimates of the risk are of the form of a U-statistic, results of the theory of U-processes are required for…

讨论：风险最小化中的局部 Rademacher 复杂度与预言不等式AI 解读

In this magnificent paper, Professor Koltchinskii offers general and powerful performance bounds for empirical risk minimization, a fundamental principle of statistical learning theory.Since the elegant pioneering work of Vapnik and Chervonenkis in the early 1970s, various such bounds have been known that relate the performance of empirical risk minimizers to combinatorial and geometrical features of the class over which the minimization is performed.This area of research…