权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

AF: Small: Foundations for Data-driven Algorithmics

AF：小：数据驱动算法的基础

基本信息

批准号：
1816874
负责人：
Yaron Singer
金额：
$ 49.99万
依托单位：
Harvard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-15 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816874&HistoricalAwards=false
关键词：
AF Small Foundations Data driven

项目摘要

The traditional approach in optimization assumes that the underlying objective is known, but in many real-life applications, the true objectives are not known and learned from data. This gap between theory and practice turns out to be quite dramatic, and leaves us without guarantees on the performance of optimization algorithms in such applications. The goal of this project is to develop a theory for algorithms whose input (i.e., the objective) is learned from data, and design algorithms that perform well in these settings. The technical challenges in this space are highly non-trivial, but their solution would dramatically impact our thinking in computer science and result in major advancements in AI. The project develops courses in optimization and data science that foster an interdisciplinary approach. The project will involve mentoring undergraduate and graduate students from underrepresented groups and promote an open access research culture. The investigator will develop new interdisciplinary connections through courses, seminars, and workshops with the goal of promoting a discipline of researchers working on algorithms for the information age.In light of a recent line of impossibility results initiated by the investigator, the goal of this project is to investigate alternative notions of optimization that can facilitate desirable guarantees for data-driven optimization. The first direction in this project considers optimization from adaptive samples. The general notion of adaptivity is surprisingly under-explored, and advancement on this front can have a tremendous impact both on theory and applications. A complementary direction is to consider algorithms that are given samples on a training datasets, and seek to approximate the optimal solution of the testing dataset, drawn from the same distribution. Finally, the last direction considered is that of optimization from pairwise comparisons.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

传统的优化方法假设潜在的目标是已知的，但在许多实际应用中，真正的目标并不是已知的，也不是从数据中学习的。理论和实践之间的这种差距被证明是相当巨大的，并且让我们无法保证优化算法在这种应用中的性能。这个项目的目标是开发一种算法的理论，其输入(即目标)是从数据中学习的，并设计在这些环境中表现良好的算法。这个领域的技术挑战非常重要，但他们的解决方案将极大地影响我们在计算机科学方面的思维，并导致人工智能的重大进步。该项目开发了优化和数据科学课程，以促进跨学科方法。该项目将包括指导来自代表性不足群体的本科生和研究生，并促进开放获取研究文化。研究人员将通过课程、研讨会和研讨会发展新的跨学科联系，目的是促进研究人员致力于信息时代的算法的学科。鉴于最近由研究人员发起的一系列不可能的结果，该项目的目标是调查替代的优化概念，以促进数据驱动优化的理想保证。这个项目的第一个方向是考虑从自适应样本中进行优化。令人惊讶的是，适应性的一般概念没有得到充分的研究，这方面的进展可能会对理论和应用产生巨大的影响。一个互补的方向是考虑在训练数据集上给予样本的算法，并寻求从相同分布中提取的测试数据集的最优解的近似。最后，考虑的最后一个方向是从配对比较中进行优化。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。