CAREER: Robustness, Active Learning, Sparsity, and Fairness in Classification

职业：分类中的鲁棒性、主动学习、稀疏性和公平性

• 批准号: 2239376
• 负责人: Jie Shen
• 金额: $ 59.07万
• 依托单位: Stevens Institute of Technology
• 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: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239376&HistoricalAwards=false
• 关键词: CAREER; Robustness; Active; Learning; Sparsity

Learning and making accurate inferences from complex data is a core task in modern data analytics. Over the past few decades, a large volume of efficient algorithms has been developed and tested in a broad range of science and engineering problems. However, rigorous analysis of these algorithms often relies on simplified assumptions about the structure of the data, which may not capture the real characteristics. The project aims to go beyond standard theoretical frameworks by developing new theories and algorithms to address pressing challenges arising from contemporary applications, such as adversarial data contamination. In particular, this project will study and lay solid theoretical foundations for the classification problem, which plays a fundamental role in machine learning. A crucial educational component of the project involves the development of a new undergraduate program in artificial intelligence and machine learning that has the potential to inspire a transformation of nationwide STEM education. Furthermore, the principal investigator will continue to mentor undergraduate and graduate students.The project will address several fundamental questions in classification for which there is a large gap in our current understanding. A wide range of modern tools will be leveraged to design new algorithms that can tolerate adversarial corruptions in the data, mitigate data annotation costs, circumvent the curse of high dimensionality, and fortify models with fairness guarantees. Complementary to the algorithmic results, the project will also develop information-theoretic and statistical-query lower bounds to broaden the understanding of fundamental limits posed by the practical constraints. The comprehensive investigation of these problems and their interplay will lead to new analytic and algorithmic tools, enrich various areas (such as learning theory, statistics, and optimization), and build new bridges between them.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.

从复杂数据中学习并进行准确的推论是现代数据分析中的核心任务。在过去的几十年中，已经在广泛的科学和工程问题中开发和测试了大量有效的算法。但是，对这些算法的严格分析通常依赖于对数据结构的简化假设，这些假设可能无法捕获实际特征。该项目的目标是通过开发新的理论和算法来解决标准理论框架，以应对当代应用（例如对抗数据污染）引起的紧迫挑战。特别是，该项目将研究并为分类问题奠定坚实的理论基础，该基础在机器学习中起着基本作用。该项目的关键教育部分涉及制定人工智能和机器学习新的本科计划，该计划有可能激发全国性STEM教育的转变。此外，首席研究人员将继续指导本科生和研究生。该项目将解决分类中的几个基本问​​题，在我们目前的理解中存在很大的差距。将利用广泛的现代工具来设计新算法，这些算法可以忍受数据中的对抗性损坏，减轻数据注释成本，规避高维度的诅咒以及具有公平性保证的强化模型。与算法结果互补，该项目还将开发信息理论和统计质量质量下限，以扩大对实际约束所带来的基本限制的理解。对这些问题及其相互作用的全面调查将导致新的分析和算法工具，丰富各个领域（例如学习理论，统计和优化），并在它们之间建立新的桥梁。这项奖项反映了NSF的法定任务，并通过该基金会的知识绩效和广泛的影响来评估NSF的法定任务，并被认为值得通过评估。