Collaborative Research: Transfer Learning for Large-Scale Inference: General Framework and Data-Driven Algorithms

迁移学习提供了利用在不同背景下或不同人群中进行的相关研究数据的关键技术。它在整合基因组学、神经影像学、计算机视觉和信号处理等领域有着广泛的应用。这项研究工作将为科学研究人员提供新的工具，他们经常收集和分析不同来源和平台的高维和复杂数据。该项目旨在开发新的分析工具，通过提供更多信息和可解释的科学发现来改进传统方法。开发的迁移学习算法可以从不同的数据类型和不同的研究中可靠地提取和联合收割机知识，将有助于解决基因组学应用中的重要问题。将开发方便用户的软件包并向公众提供。科学研究人员可以使用这些工具将分散和异构的数据源转化为新的知识和医疗效益。这将有助于更好地了解各种遗传因素在复杂疾病中的作用，并以具有成本效益的方式加快新药和治疗方法的开发。大规模推理的迁移学习旨在提取和迁移从相关源域中学习到的知识，以辅助目标域中数千甚至数百万个参数的同时推理。我们的目标是开发一个通用框架，以了解迁移学习在广泛的大规模推理问题中的好处和注意事项，包括稀疏估计，错误发现率分析，稀疏线性判别分析和高维回归。我们的研究解决了迁移学习中的两个关键问题：（a）应该迁移什么？(b)如何转移和防止消极学习？我们致力于实现三大研究目标。第一个是开发一类计算效率高且鲁棒的高维稀疏推理迁移学习算法。一般的策略是先通过辅助数据学习高维对象的局部稀疏结构，然后通过在相应的坐标上自适应地设置不同的权重或设置不同的阈值，将结构知识应用于目标域。第二个是形式化一个决策理论框架，用于高维迁移学习，适用于稀疏和非稀疏制度。沿着这个方向，我们的目标是发展一类核化的非参数经验贝叶斯方法的数据共享收缩估计和多重检验。第三个奖项是为了解决使用新开发方法的重要基因组学应用所带来的紧迫需求和新挑战。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。

Transfer learning provides crucial techniques for utilizing data from related studies that are conducted under different contexts or on diverse populations. It is an important topic with a wide range of applications in integrative genomics, neuroimaging, computer vision and signal processing. This research work will provide new tools to scientific researchers who routinely collect and analyze high dimensional and complex data across different sources and platforms. This project aims to develop new analytical tools to improve conventional methods by delivering more informative and interpretable scientific findings. The developed transfer learning algorithms, which can reliably extract and combine knowledge from diverse data types and across different studies, will help address important issues from genomics applications. User-friendly software packages will be developed and made publicly available. Scientific researchers can use the tools to translate dispersed and heterogeneous data sources into new knowledge and medical benefits. This will help improve the understanding of the role of various genetic factors in complex diseases, and accelerate the development of new medicines and treatments in a cost-effective way. Transfer learning for large-scale inference aims to extract and transfer the knowledge learned from related source domains to assist the simultaneous inference of thousands or even millions of parameters in the target domain. We aim to develop a general framework to gain understanding of the benefits and caveats of transfer learning in a wide range of large-scale inference problems including sparse estimation, false discovery rate analysis, sparse linear discriminant analysis and high-dimensional regression. Our research addresses two key issues in transfer learning: (a) What should be transferred? (b) How to transfer and prevent negative learning? We aim to pursue three major research goals. The first is to develop a class of computationally efficient and robust transfer learning algorithms for high-dimensional sparse inference. The general strategy is to first learning the local sparsity structure of the high-dimensional object through auxiliary data and then apply the structural knowledge to the target domain by adaptively placing differential weights or setting varied thresholds on corresponding coordinates. The second is to formalize a decision-theoretic framework for high-dimensional transfer learning that is applicable across the sparse and non-sparse regimes. Along this direction, we aim to develop a class of kernelized nonparametric empirical Bayes methods for data-sharing shrinkage estimation and multiple testing. The third is to address the urgent needs and new challenges arising from important genomics applications using the newly developed methods.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.