CAREER: Algorithms for Unsupervised Learning

这项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。虽然在目前的文献中最常见的保证是局部最优的解决方案空间中，这个项目的目的是使用更强的性能标准，如量化的比例上的界限，其中学习的解决方案的成本超过了全球最优，既指导新算法的发展，并比较现有的。该项目将专注于两个规范的无监督学习任务：层次聚类和学习有向概率（贝叶斯）网的结构。这两种模型已经广泛用于分析海量数据集;更好的算法将提高其有效性和可靠性，并将涉及可能更广泛用于其他机器学习和统计任务的技术工具。该研究项目的成果将被纳入一门新课程，重点是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.