CAREER: New Approaches for Ranking in Machine Learning

在许多行业中，决策是基于大量数据的，其中可能采取的行动的排名列表决定了如何使用有限的资源。在过去的十年中，用于排名的机器学习算法被设计用于解决优先级问题。这些算法根据拥有某个属性的概率对一组对象进行排名;例如，我们可以根据明年着火的概率对一组沙井进行排名。然而，目前的算法解决排名问题的近似而不是准确的，这些近似算法可能会很慢，而且他们没有考虑到许多特定的应用problems.The本项目的目标包括：I）通过开发一个工具箱的算法技术的基础上混合整数优化技术的排名问题的精确解决方案。II）通过显示排序问题与更容易的分类问题的基本等价性来更快地找到解决方案，这些问题可以更快地解决一个数量级。三、为新的结构化问题建立框架。第一个框架涉及具有与能量域相关的图结构的排名问题。第二个框架处理推荐系统产生的顺序预测问题，也在医疗领域的应用。通过与行业的合作，所提出的方法正在应用于几个不同的领域，包括预防纽约市电网上的严重事件（火灾和爆炸）。

In numerous industries, decisions are based on large amounts of data, where a ranked list of possible actions determines how limited resources will be spent. Over the last decade, machine learning algorithms for ranking have been designed to address prioritization problems. These algorithms rank a set of objects according to the probability to possess a certain attribute; for example, we might rank a set of manholes in order of their probability to catch fire next year. However, current algorithms solve ranking problems approximately rather than exactly, and these approximate algorithms can be slow; furthermore they do not take into account many application-specific problems.The goals of this project include: I) Finding exact solutions to ranking problems by developing a toolbox of algorithmic techniques based on mixed-integer optimization technology. II) Finding solutions faster by showing a fundamental equivalence of ranking problems to easier classification problems that can be solved an order of magnitude faster. III) Developing frameworks for new structured problems. The first framework pertains to ranking problems that have a graph structure that are relevant to the energy domain. The second framework handles a sequential prediction problem arising from recommender systems, with applications also in the medical domain.Through collaboration with industry, the proposed methods are being applied in several different areas, including the prevention of serious events (fires and explosions) on NYC's electrical grid.