Minipatch Learning for Selection, Stability, Inference, and Scalability

现在，几乎每个行业和学术领域都在收集大量数据。揭示这些数据中隐藏的见解是解决重大科学挑战的关键，例如了解大脑如何工作，发现导致癌症和阿尔茨海默病等疾病的机制，以及应对气候变化等。但是，在复杂和庞大的数据中发现关键特征和重要关系带来了重大的统计和计算挑战。研究者的目标是为这项任务开发新的统计机器学习方法和理论，将庞大的数据集分解成称为minipatch的小随机子集，以促进更快的计算和提高统计效率。这些新方法将在开源软件中实现，并应用于基因组学和神经科学领域的庞大生物医学数据集。该项目将为本科生和研究生提供培训和专业发展机会。发现生物医学中常见的复杂和庞大数据中的关键特征和重要关系不仅提出了重大的计算挑战，而且提出了关键的统计挑战。为了应对这些挑战，研究者计划开发一种名为“小补丁学习”的新框架。受随机森林、高维统计中的稳定性方法和随机优化策略的成功启发，研究者将从许多随机的微小子集中构建集合，这些子集包括观测值和特征或变量，称为微补丁。虽然集成学习策略通常用于监督机器学习，但研究者将使用微补丁学习来完成特征选择、特征重要性的模型不可知推断以及通过图形模型学习特征之间的关系等任务。这种方法在非常小的数据子集上进行训练，有望节省大量的计算和内存。研究者的目的是在理论上和经验上表明，这种策略也具有显著的统计优势。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。

Massive amounts of data are now collected by nearly every industry and academic discipline. Uncovering the hidden insights in such data holds the key to major scientific challenges such as understanding how the brain works, discovering mechanisms leading to diseases such as cancer and Alzheimer's disease, and combating climate change, among many others. But discovering key features and important relationships in complex and huge data poses major statistical and computational challenges. The investigator aims to develop new statistical machine learning approaches and theory for this task that break up huge data sets into small random subsets called minipatches to facilitate both faster computation and improved statistical efficiency. The new methods will be implemented in open-source software and applied to huge biomedical datasets in genomics and neuroscience. The project will provide undergraduate and graduate students training and professional development opportunities.Discovering key features and important relationships in complex and huge data commonly found in biomedicine poses not only major computational challenges but also critical statistical challenges. To tackle these challenges, the investigator plans to develop a new framework termed minipatch learning. Inspired by the successes of random forests, stability approaches in high-dimensional statistics, and stochastic optimization strategies, the investigator will build ensembles from many random tiny subsets of both observations and features or variables called minipatches. While ensemble learning strategies are commonly used in supervised machine learning, the investigator will use minipatch learning for the tasks of feature selection, model-agnostic inference for feature importance, and learning relationships amongst features through graphical models. The approach, which trains on very tiny subsets of the data, is expected to have dramatic computational and memory savings. The investigator aims to show both theoretically and empirically that such a strategy poses significant statistical advantages as well.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.