RI: Small: Collaborative Research: Statistical ranking theory without a canonical loss

RI：小：协作研究：没有典型损失的统计排名理论

• 批准号: 1320894
• 负责人: Pradeep Ravikumar
• 金额: $ 22.36万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320894&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Statistical

The problem of ranking objects occupies a central place in key technologies such as web search and recommendation systems. These technologies have a tremendous daily impact on the lives of millions of people. Moreover, the enormous scale of data on the web makes the use of machine learning especially attractive in constructing ranking algorithms. A huge amount of research effort has been devoted to developing efficient ranking algorithms that can deal with a variety of data sets encountered in web search and recommendation systems.This project develops unifying mathematical theory that will provide a basis for understanding and categorizing existing algorithms and, more importantly, lead to deeper insights and new algorithms for the problem of learning to rank. The investigators also apply ranking algorithms to new domains. For example, ranking chemical reactions based on their plausibility will help chemists discover much-needed reaction bases for technologies such as carbon dioxide reduction, and conversion of natural gas into gasoline. Fundamental advances in the statistical theory of ranking will be incorporated into undergraduate and graduate courses. Data sets and software developed will be made freely available to the scientific community. The investigators will also organize a workshop with a focus on interdisciplinary participation and involvement of under-represented groups in computer science and statistics.The primary technical challenge in developing statistical ranking theory is the absence of a universally agreed-upon loss functions for ranking. This is in contrast to classic machine learning problems such as classification and regression, where there are only a few natural possibilities for the loss function and these are well-understood theoretically. The project addresses this gap by investigating how different loss functions for ranking affect fundamental theoretical properties such as learnability, and by creating a theory of convex surrogates that is applicable when loss functions abound. The project re-examines existing statistical literature on ranking with a computational lens. This will enable development of flexible and efficient plug-in decision rules that model the conditional probability of labels given inputs.By incorporating the results of this research into courses and survey articles, the PIs help train a new generation of machine learning researchers and practitioners who will view ranking as a learning problem on par with classification and regression in mathematical depth as well as practical importance. Theoretical guidance for practitioners formulating new algorithms for ranking will improve the most common applications on the web.

排名对象的问题在关键技术（例如Web搜索和推荐系统）中占据了基础。这些技术对数百万人的生活产生巨大影响。此外，网络上的大量数据规模使机器学习的使用在构建排名算法时特别有吸引力。 大量的研究工作已致力于开发有效的排名算法，这些算法可以处理网络搜索和推荐系统中遇到的各种数据集。该项目开发了统一的数学理论，该理论将为现有的算法提供基础，以理解和分类现有的算法，更重要的是，更重要的是，可以使洞察力和新的Algoriths corge for Learne Comply sar nar Gressens cart an Carl an Carl an Carl an Cart an Char the Algoriths。研究人员还将排名算法应用于新领域。 例如，基于其合理性的化学反应对化学反应的排名将有助于化学家发现急需的反应基库，例如二氧化碳还原碳，并将天然气转化为汽油。排名统计理论的基本进步将纳入本科和研究生课程。开发的数据集和软件将免费提供给科学界。研究人员还将组织一个研讨会，重点是跨学科的参与以及代表性不足的群体参与计算机科学和统计的参与。开发统计排名理论的主要技术挑战是缺乏普遍商定的排名损失功能。这与经典的机器学习问题（例如分类和回归）形成鲜明对比，后者只有少数自然可能性的损失功能，这些可能性在理论上是善良的。该项目通过调查排名的不同损失功能如何影响基本理论属性，例如可学习性，以及创建凸构代替代理论，该理论在损失功能多数时适用，从而解决了这一差距。 该项目重新检查有关使用计算镜头排名的现有统计文献。 这将使能够开发灵活，高效的插入决策规则，以模拟给定输入的标签的条件概率。通过将本研究的结果纳入课程和调查文章中，PIS帮助培训新一代的机器学习研究人员和从业人员，他们将认为排名是与分类和回归在数学深度以及实践中的分类和回归方面的学习问题。 制定新算法的从业者的理论指南将改善网络上最常见的应用程序。