Collaborative Research: New Theory and Methods for High-Dimensional Multi-Task and Transfer Learning Inference

合作研究：高维多任务和迁移学习推理的新理论和新方法

• 批准号: 2324490
• 负责人: Jinchi Lv
• 金额: $ 15万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324490&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Theory; Methods

In many modern big data applications, data is often collected from diverse sources. To improve prediction or clustering accuracy, multi-task learning and transfer learning techniques have been employed widely to leverage the possible similarities across different tasks. For example, it is of crucial importance to develop reliable inference procedures for applications such as the Federal Reserve Economic Database (FRED) to identify latent factors and individual compositions of significant macroeconomic variables associated with typical macroeconomic indicators. Similarly, for databases like the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the National Alzheimer's Coordinating Center (NACC), early detection and risk factor identification of dementia, such as Alzheimer's disease, is vital. In these contexts, different economic indicators or patients in different hospitals may share certain similarities. However, it remains largely unclear how to develop flexible inference procedures for high-dimensional multi-task learning and transfer learning. The research project can have potentially significant impacts across diverse fields, including economics, business, engineering, and medicine. These new theoretical and methodological developments will build rigorous statistical foundations for high-dimensional multi-task and transfer learning inference under practical conditions, and provide interpretable, flexible, and robust tools for various researchers and practitioners in data science applications. The project also provides research training opportunities for graduate students. High-dimensional multi-task and transfer learning inference under both supervised and unsupervised settings are challenging and important topics in statistical machine learning and data science. In this project, the PIs address these fundamental challenges by conducting systematic studies to develop novel methodologies, algorithms, theories, and applications through three interrelated aims. First, the PIs plan to investigate high-dimensional manifold-based multi-task learning inference, which involves learning a shared representation of multiple tasks that lie on a low-dimensional manifold. the project will develop robust and scalable algorithms that can handle high-dimensional data and incorporate manifold constraints to provide much-needed inference tools for the latent singular value decomposition (SVD) structures. Second, the PIs plan to tackle high-dimensional robust multi-task clustering inference, where the goal is to simultaneously cluster data from multiple tasks in the presence of outliers and noise. The project will develop novel robust multi-task clustering algorithms that can handle high-dimensional data and outlier tasks. Third, the PIs plan to investigate high-dimensional adaptive and robust multi-task learning and transfer learning from similar linear representations, which involves learning a shared representation of multiple tasks that share similar linear structures. Here, the project will develop adaptive and robust algorithms that can handle high-dimensional data, adapt to different noise levels, and transfer knowledge across similar linear representations.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.

在许多现代的大数据应用中，数据通常是从不同来源收集的。为了提高预测或聚类的准确性，已广泛采用多任务学习和转移学习技术来利用不同任务之间可能的相似性。例如，为诸如美联储经济数据库（FRED）等应用程序开发可靠的推理程序至关重要，以确定与典型宏观经济指标相关的重要宏观经济变量的潜在因素和个体组成。同样，对于诸如阿尔茨海默氏病神经影像学计划（ADNI）和国家阿尔茨海默氏症协调中心（NACC）（NACC）之类的数据库，早期发现和危险因素鉴定痴呆症，例如阿尔茨海默氏病，至关重要。在这些情况下，不同医院的不同经济指标或患者可能具有某些相似之处。但是，在很大程度上尚不清楚如何为高维多任务学习和转移学习开发灵活的推理程序。该研究项目可能会在包括经济，商业，工程和医学在内的各种领域产生重大影响。这些新的理论和方法论发展将为在实际条件下的高维多任务和转移学习推论建立严格的统计基础，并为数据科学应用中的各种研究人员和从业人员提供可解释，灵活和强大的工具。该项目还为研究生提供了研究培训机会。在受监督和无监督的设置下，高维的多任务和转移学习推论在统计机器学习和数据科学中都是具有挑战性和重要主题。在该项目中，PI通过进行系统研究来解决这些基本挑战，以通过三个相互关联的目标开发新的方法，算法，理论和应用。首先，PIS计划研究基于高维的多任务学习推论，这涉及学习在低维流形上的多个任务的共同表示。该项目将开发可靠的可扩展算法，这些算法可以处理高维数据并包含歧管约束，从而为潜在的奇异值分解（SVD）结构提供了急需的推理工具。其次，PIS计划解决高维鲁棒的多任务聚类推断，其目标是在存在异常值和噪声的情况下同时从多个任务中群集数据。该项目将开发出新颖的可靠多任务聚类算法，这些算法可以处理高维数据和外部任务。第三，PIS计划研究高维自适应和健壮的多任务学习和从相似线性表示的转移学习，这涉及学习共享相似线性结构的多个任务的共享表示。在这里，该项目将开发自适应且可靠的算法，这些算法可以处理高维数据，适应不同的噪声水平，并跨相似线性表示转移知识。该奖项反映了NSF的法定任务，并被认为是通过基金会的评估来支持的，并且被认为值得支持。智力优点和更广泛的影响审查标准。