III: Small: Collaborative Research: Structured Methods for Multi-Task Learning

III：小：协作研究：多任务学习的结构化方法

• 批准号: 1615035
• 负责人: Shuiwang Ji
• 金额: $ 24.69万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615035&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Structured

The ability of human to learn from and transfer knowledge across related learning tasks enables us to grasp complex concepts from only a few examples. For instance, a three-year old child is able to discriminate chairs from tables without having been exposed to hundreds of different examples. In contrast, computer learning programs typically require training on a large number of examples in order to achieve similar levels of recognition. This prompts the study of multi-task learning in which multiple related tasks are learned simultaneously, thereby facilitating inter-task knowledge transfer. However, most multi-task learning studies are restricted to problems with well-defined tasks and structures. This project aims at developing algorithms and tools (including open source software) to attack problems that are not traditionally treated, but can potentially be reformulated and solved more effectively by multi-task learning. This allows a broad class of challenging machine learning problems to benefit from multi-task learning techniques. This project also develops a new curriculum that incorporates the proposed research into classroom. In addition, this project will allow the PIs to continue the ongoing efforts of actively recruiting and advising students from under-represented groups. To achieve these goals, this project focuses on an innovative, integrated research and education plan that includes the following components: (1) providing principled guidelines for reformulating problems into the multi-task learning formalism; (2) developing robust and clustered multi-task learning models to identify and prevent false interactions among unrelated tasks; (3) developing sparsity-inducing multi-task learning models to capture richly structured task interactions; (4) developing high-order multi-task learning models to capture task relatedness from interactions between features; and (5) investigating computational algorithms and theoretical properties of multi-task learning. The outcome of this project includes the capabilities of reformulating diverse machine learning problems into the multi-task learning framework and providing radically new ways to attack challenging problems that cannot be solved effectively by traditional methods. The systematic study of multi-task learning in this project is expected to generate novel reformulations, structured mathematical models, efficient optimization algorithms, and principled theoretical analyses, which will lead to significant practical and theoretical advances in multi-task learning.

人类从相关学习任务中学习和迁移知识的能力使我们能够仅从几个例子中掌握复杂的概念。例如，一个三岁的孩子无需接触过数百个不同的例子就能够区分椅子和桌子。相比之下，计算机学习程序通常需要对大量示例进行训练才能达到相似的识别水平。这促进了多任务学习的研究，其中同时学习多个相关任务，从而促进任务间的知识迁移。然而，大多数多任务学习研究仅限于具有明确定义的任务和结构的问题。该项目旨在开发算法和工具（包括开源软件）来解决传统上未处理但可以通过多任务学习更有效地重新表述和解决的问题。这使得一系列具有挑战性的机器学习问题可以从多任务学习技术中受益。该项目还开发了一种新课程，将拟议的研究纳入课堂。此外，该项目将使 PI 能够继续积极招募来自代表性不足群体的学生并为其提供建议。为了实现这些目标，该项目侧重于创新、综合的研究和教育计划，包括以下组成部分：（1）为将问题重新表述为多任务学习形式提供原则性指导； （2）开发稳健的集群多任务学习模型，以识别和防止不相关任务之间的错误交互； （3）开发稀疏性多任务学习模型以捕获丰富的结构化任务交互； （4）开发高阶多任务学习模型，从特征之间的交互中捕获任务相关性； (5) 研究多任务学习的计算算法和理论特性。该项目的成果包括将各种机器学习问题重新表述为多任务学习框架的能力，并提供全新的方法来解决传统方法无法有效解决的挑战性问题。该项目对多任务学习的系统研究预计将产生新颖的重新表述、结构化数学模型、高效的优化算法和原则性的理论分析，这将导致多任务学习的重大实践和理论进步。