论文标题
学习有限的子集的$ l_p $
Learning bounded subsets of $L_p$
论文作者
论文摘要
我们研究学习问题，其中基础类是$ l_p $的有限子集，而目标$ y $属于$ l_p $。以前，仅当$ p = \ infty $时，在此类界假设下才知道最小值样本复杂性估计值。我们提出了一个鲜明的样本复杂性估计，该估算值均适用于任何$ p> 4 $。它基于适合重尾问题的学习程序。
We study learning problems in which the underlying class is a bounded subset of $L_p$ and the target $Y$ belongs to $L_p$. Previously, minimax sample complexity estimates were known under such boundedness assumptions only when $p=\infty$. We present a sharp sample complexity estimate that holds for any $p > 4$. It is based on a learning procedure that is suited for heavy-tailed problems.
