Minipatch Learning for Selection, Stability, Inference, and Scalability

用于选择、稳定性、推理和可扩展性的小补丁学习

• 批准号: 2210837
• 负责人: Genevera Allen
• 金额: $ 26.11万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210837&HistoricalAwards=false
• 关键词: Minipatch; Learning; Selection; Stability; Inference

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.

现在几乎每个行业和学术学科都收集了大量的数据。揭示这些数据中隐藏的见解是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，在复杂而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。研究人员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为称为minipatches的小的随机子集，以促进更快的计算和提高统计效率。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的大型生物医学数据集。该项目将为本科生和研究生提供培训和专业发展的机会。在生物医学中常见的复杂和庞大的数据中发现关键特征和重要关系不仅带来了重大的计算挑战，而且还带来了关键的统计挑战。为了应对这些挑战，研究人员计划开发一个名为迷你手表学习的新框架。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，研究人员将从许多随机的微小子集（观测值和特征或变量，称为minipatches）中构建集合。虽然集成学习策略通常用于有监督的机器学习，但研究人员将使用迷你补丁学习来完成特征选择，特征重要性的模型不可知推理以及通过图形模型学习特征之间的关系。该方法在非常小的数据子集上进行训练，预计将节省大量的计算和内存。该奖项反映了NSF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。