CAREER: Algorithms for Unsupervised Learning

本研究的目标是为核心机器学习任务开发具有严格性能保证的算法。虽然目前文献中最常见的保证是解空间的局部最优性，但本项目旨在使用更强的性能标准，例如学习解的成本超过全局最优成本的比率的定量界限，以指导新算法的开发并比较现有算法。该项目将专注于两个典型的无监督学习任务：分层聚类和学习有向概率（贝叶斯）网络的结构。这两种模型都已被广泛用于分析海量数据集；更好的算法将提高它们的有效性和可靠性，并将涉及可能在其他机器学习和统计任务中有更广泛用途的技术工具。该研究项目的结果将整合到一门新的课程中，该课程侧重于机器学习的算法方面；由此产生的教育材料将提供给学术界。

The goal of this research is to develop algorithms with rigorous performance guarantees for core machine learning tasks. Although the most common guarantee in the current literature is that of local optimality in the solution space, this project aims to use stronger performance criteria, such as quantitative bounds on the ratio by which the cost of the learned solution exceeds that of the global optimum, both to guide the development of new algorithms and to compare existing ones. This project will focus on two canonical unsupervised learning tasks: hierarchical clustering and learning the structure of directed probabilistic (Bayesian) nets. Both models are already in widespread use for analyzing massive data sets; better algorithms will increase their effectiveness and reliability, and will involve technical tools that are likely to be of broader use for other machine learning and statistical tasks. The results of this research project will be integrated into a new course that focuses on algorithmic aspects of machine learning; the resulting educational materials will be made available to the academic community.