CAREER: New Approaches for Ranking in Machine Learning

在许多行业中，决策基于大量数据，其中可能采取的行动的排序列表决定了有限的资源将如何使用。在过去的十年里，用于排名的机器学习算法一直被设计来解决优先级问题。这些算法根据拥有特定属性的概率对一组对象进行排序；例如，我们可能会根据一组检修孔明年着火的概率对其进行排序。然而，目前的算法对排序问题的解决是近似的而不是精确的，并且这些近似算法可能很慢，而且它们没有考虑许多特定的应用问题。本项目的目标包括：i)通过开发基于混合整数优化技术的算法技术工具箱来找到排序问题的精确解。Ii)通过显示排序问题与可以更快地解决的更容易的分类问题的基本等价性，更快地找到解决方案。三)为新的结构化问题制定框架。第一个框架涉及对具有与能源领域相关的图形结构的问题进行排序。第二个框架处理从推荐系统中产生的序列预测问题，在医疗领域也有应用。通过与业界的合作，所提出的方法正被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.