论文标题
沟通感知的协作学习
Communication-Aware Collaborative Learning
论文作者
论文摘要
近年来，针对样本复杂性，已经对无噪声协作PAC学习的算法进行了分析和优化。在本文中，我们研究了协作性PAC学习，目的是减少沟通成本，但基本上没有对样本复杂性的惩罚。我们使用分布式提升开发了沟通有效的协作PAC学习算法。然后，我们考虑在存在分类噪声的情况下协作学习的沟通成本。作为中级步骤，我们展示了如何将协作PAC学习算法适应以处理分类噪声。有了这种见解，我们开发了沟通有效的算法，以协作PAC学习与分类噪声的强大学习。
Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. We develop communication efficient collaborative PAC learning algorithms using distributed boosting. We then consider the communication cost of collaborative learning in the presence of classification noise. As an intermediate step, we show how collaborative PAC learning algorithms can be adapted to handle classification noise. With this insight, we develop communication efficient algorithms for collaborative PAC learning robust to classification noise.
