论文标题
通过提升有效，容忍和私人学习
Efficient, Noise-Tolerant, and Private Learning via Boosting
论文作者
论文摘要
我们介绍了一个简单的框架，用于设计私人增强算法。我们提供这些算法在差异上是私人，高效且耐噪声的PAC学习者的自然条件。为了展示我们的框架，我们使用它来构建耐噪声和私人PAC学习者的大幅度半空间，其样品复杂性不取决于尺寸。 我们为我们的大幅度半空间学习者提供了两个样本复杂性界限。一个界限仅基于差异隐私，并将此保证作为确保概括的资产。首先，这说明了一种从隐私中获得PAC学习者的一般方法，这可能是独立的利益。第二界使用来自大规模分类理论（脂肪崩溃的维度）的标准技术，以匹配最知名的样本复杂性，以差异化大幅度学习半空间的私人学习，同时还可以忍受随机标签噪声。
We introduce a simple framework for designing private boosting algorithms. We give natural conditions under which these algorithms are differentially private, efficient, and noise-tolerant PAC learners. To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspaces whose sample complexity does not depend on the dimension. We give two sample complexity bounds for our large-margin halfspace learner. One bound is based only on differential privacy, and uses this guarantee as an asset for ensuring generalization. This first bound illustrates a general methodology for obtaining PAC learners from privacy, which may be of independent interest. The second bound uses standard techniques from the theory of large-margin classification (the fat-shattering dimension) to match the best known sample complexity for differentially private learning of large-margin halfspaces, while additionally tolerating random label noise.
