论文标题
从分批学习的一种一般方法
A General Method for Robust Learning from Batches
论文作者
论文摘要
在许多应用程序中，数据分批收集，其中一些是损坏甚至对抗性的。最近的工作得出了最佳的鲁棒算法，用于估计此环境中的离散分布。我们考虑从批处理学习的一般框架，并确定分类和分布估计的限制，包括连续的域，包括连续的域。在这些结果的基础上，我们为分段间隔分类的第一个强大的不可知论计算算法，以及分段 - 多项式，单调，对数 - concave和高斯混合分布估算。
In many applications, data is collected in batches, some of which are corrupt or even adversarial. Recent work derived optimal robust algorithms for estimating discrete distributions in this setting. We consider a general framework of robust learning from batches, and determine the limits of both classification and distribution estimation over arbitrary, including continuous, domains. Building on these results, we derive the first robust agnostic computationally-efficient learning algorithms for piecewise-interval classification, and for piecewise-polynomial, monotone, log-concave, and gaussian-mixture distribution estimation.
