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。虽然目前文献中最常见的保证是解空间中的局部最优性，但该项目的目标是使用更强大的性能标准，例如学习解的成本超过全局最优解的比率的量化界限，以指导新算法的开发和比较现有算法。这个项目将集中在两个典型的非监督学习任务：层次聚类和学习有向概率(贝叶斯)网的结构。这两种模型都已经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，并将涉及可能会更广泛地用于其他机器学习和统计任务的技术工具。这一研究项目的结果将被整合到一门侧重于机器学习算法方面的新课程中；由此产生的教育材料将向学术界提供。

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.