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Collaborative Research: Consistent Risk Estimation under High-Dimensional Asymptotics

合作研究：高维渐近下的一致风险估计

基本信息

批准号：
1810880
负责人：
Kamiar Rahnama Rad
金额：
$ 7万
依托单位：
CUNY Baruch College
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：
2018-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1810880&HistoricalAwards=false
关键词：
Collaborative Research Consistent Risk Estimation

项目摘要

Learning from large datasets has been the cornerstone of modern innovations and discoveries in science, medicine, and technology. Fast prediction of unseen events is a canonical goal in statistical learning. A classic approach to this end is leave-one-out cross-validation, a time-consuming routine of leaving a datum out, fitting the model on the rest, and testing it on the left out datum, repeatedly. The recent emergence of massive data has exacerbated the computational infeasibility of such approaches. Moreover, in many recent instances, the number of features per observation can be extremely large, adding another challenging facet to the fast estimation of prediction error. To overcome these problems a new set of scalable and consistent risk estimators will be developed in this project. The importance of risk estimation has motivated this project of different schemes, such as cross-validation, Stein's unbiased risk estimation (SURE), Generalized cross-validation, Akaike Information Criterion (AIC), and Bootstrap. The emergence of high-dimensional datasets has challenged most classical approaches to risk estimation. For instance, the large discrepancy between in-sample and out-of-sample prediction error, in applications involving predictions based on previously unseen features, makes it hard to rely on popular estimators, such as SURE or AIC, in high-dimensional regimes where the number of predictors is smaller than or at the same order as the number of observations. On the other hand, the information value of a datum in these regimes (as opposed to the information value of a datum in low-dimensional settings) casts doubt on the reliability of other techniques, such as 5-fold cross-validation. The project offers a novel theoretical framework to find the middle ground between scalability and reliability, and specifically, to obtain theoretically consistent and computationally efficient risk-estimation schemes under high-dimensional settings. Since risk estimation is at the core of areas including but not limited to machine learning, signal processing, medical imaging, neuroscience, and social and environmental sciences, any success in this project will lead to reliable and immediate scientific discoveries and better learning systems.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.

从大型数据集中学习一直是科学、医学和技术领域现代创新和发现的基石。快速预测未知事件是统计学习的一个典型目标。一个经典的方法是留一交叉验证，这是一个耗时的例程，需要将一个数据排除在外，在其余数据上拟合模型，并在排除的数据上重复测试。最近大量数据的出现加剧了这种方法的计算不可行性。此外，在最近的许多情况下，每次观察的特征数量可能非常大，这给预测误差的快速估计增加了另一个挑战性方面。为了克服这些问题，一套新的可扩展的和一致的风险估计将在这个项目中开发。风险估计的重要性促使该项目采用了不同的方案，例如交叉验证、Stein的无偏风险估计（SURE）、广义交叉验证、Akaike信息准则（AIC）和Bootstrap。高维数据集的出现对大多数经典的风险估计方法提出了挑战。例如，在涉及基于先前未见过的特征的预测的应用中，样本内和样本外预测误差之间的巨大差异使得在预测因子的数量小于观测值的数量或与观测值的数量处于相同数量级的高维区域中难以依赖流行的估计器，例如SURE或AIC。另一方面，在这些制度中的数据的信息值（相对于低维设置中的数据的信息值）对其他技术的可靠性表示怀疑，例如5折交叉验证。该项目提供了一个新的理论框架，以找到可扩展性和可靠性之间的中间地带，特别是在高维设置下获得理论上一致和计算效率高的风险估计方案。由于风险估计是机器学习、信号处理、医学成像、神经科学以及社会和环境科学等领域的核心，该项目的任何成功都将导致可靠和即时的科学发现和更好的学习系统。该奖项反映了NSF的法定使命，并通过利用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。