Flexible Statistical Modeling

灵活的统计建模

• 批准号: 2113389
• 负责人: Robert Tibshirani
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113389&HistoricalAwards=false
• 关键词: Flexible; Statistical; Modeling

Statistical learning techniques have made significant progress in the past 15-20 years. Some representative areas include neural networks, applied regression, classification, and clustering. As a result of these developments, a powerful collection of adaptive regression and classification techniques are now available and can be applied to a wide range of important science and engineering areas. Some typical applications include medical diagnosis, bioinformatics, chemical process control, and face recognition. The focus of this project is on high-dimensional statistics and data science. This work will help scientists working in biotechnology and other areas to interpret and uncover important patterns in large-scale data sets. This research will also help scientists and doctors discover the biological bases of many diseases, and improve prognosis and treatment selection for patients. The project will provide research training opportunities for graduate students. This project includes four main thrusts in the area of supervised learning. In the first thrust, the investigator will build feature-efficient, or lean, models that depend on only a small number of unique features. The investigator will develop COVID-19 case forecasting through customized training and collaboration with a team in the second thrust. In the third thrust, the investigator will develop a model-free approach to the challenge of local feature importance via building a convex region around a point for prediction. The fourth thrust focuses on improving the large-scale computation of l1- regularized models.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.

统计学习技术在过去15-20年中取得了重大进展。一些代表性的领域包括神经网络、应用回归、分类和聚类。作为这些发展的结果，一组强大的自适应回归和分类技术现在是可用的，并且可以应用于广泛的重要科学和工程领域。一些典型的应用包括医学诊断、生物信息学、化学过程控制和人脸识别。这个项目的重点是高维统计和数据科学。这项工作将帮助从事生物技术和其他领域的科学家解释和揭示大规模数据集中的重要模式。这项研究还将帮助科学家和医生发现许多疾病的生物学基础，并改善患者的预后和治疗选择。该项目将为研究生提供研究培训机会。该项目包括监督学习领域的四个主要重点。在第一个推力中，研究者将构建特征高效或精简的模型，这些模型仅依赖于少数独特的特征。在第二阶段，研究员将通过定制培训和与团队合作开发COVID-19病例预测。在第三个重点中，研究者将开发一种无模型的方法，通过在预测点周围建立一个凸区域来解决局部特征重要性的挑战。第四个重点是改进l1-正则化模型的大规模计算。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Cooperative learning for multiview analysis.

• DOI: 10.1073/pnas.2202113119
• 发表时间: 2022-09-20
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1