CAREER: Harness the Big Data via Large-Scale Lifelong Learning

职业：通过大规模终身学习利用大数据

• 批准号: 1749940
• 负责人: Jiayu Zhou
• 金额: $ 55.17万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749940&HistoricalAwards=false
• 关键词: CAREER; Harness; Big; Data; via

Recent advances in big data infrastructures and algorithm foundations have unleashed a torrent of data being collected and stored in distributed data centers all over the world. The ever-increasing availability of these massive datasets leads to many machine learning tasks that are inherently related. Therefore transfer learning paradigms have been developed in the past decade to perform knowledge transfer among tasks to improve their generalization performance. This project will develop a suite of large-scale lifelong learning methods to address significant challenges from knowledge transfer on big data. The algorithms and tools developed in this project will directly impact biomedical informatics and intelligent transportation systems, as they will be used to build personalized predictive models from electronic medical records and traffic state models from big traffic data. The success of this project will be used to develop a new curriculum that incorporates research into the classroom and provides students from under-represented groups with opportunities to participate in machine learning research. The properties of velocity, volume, variability, and variety that characterize big data have imposed significant challenges in the traditional lifelong learning approaches. This project will advance lifelong learning by (1) developing a distributed life-long learning framework to enable online knowledge transfer on large-scale distributed datasets; (2) designing effective methods to track temporal drifting in the task relationship, and leverage human knowledge via interactive transfer; and (3) investigating strategies that enable the distributed life-long learning to handle heterogeneities from both feature spaces and learning tasks. The results of this project will have an immediate and strong impact on Big Data theoretical and algorithmic foundations, by enabling a large-scale lifelong learning framework readily available for many Big Data analytics. All findings, publications, software, and data will be made publicly available at the project website: http://jiayuzhou.github.io/projects/career.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.

大数据基础设施和算法基础的最新进展释放了大量数据，这些数据被收集并存储在世界各地的分布式数据中心。这些大规模数据集的可用性不断增加，导致许多机器学习任务本质上是相关的。因此，在过去的十年中，迁移学习范式已经发展到执行任务之间的知识转移，以提高其泛化性能。该项目将开发一套大规模的终身学习方法，以应对大数据知识转移带来的重大挑战。该项目中开发的算法和工具将直接影响生物医学信息学和智能交通系统，因为它们将用于从电子病历中构建个性化预测模型，并从大交通数据中构建交通状态模型。该项目的成功将用于开发一个新的课程，将研究纳入课堂，并为代表性不足的群体的学生提供参与机器学习研究的机会。大数据的速度、容量、可变性和多样性等特性对传统的终身学习方法提出了重大挑战。该项目将通过以下方式促进终身学习：（1）开发分布式终身学习框架，以实现大规模分布式数据集上的在线知识转移;（2）设计有效的方法来跟踪任务关系中的时间漂移，并通过交互式转移来利用人类知识;以及（3）研究使分布式终身学习能够处理来自特征空间和学习任务的异质性的策略。该项目的结果将对大数据理论和算法基础产生直接而强烈的影响，通过为许多大数据分析提供一个大规模的终身学习框架。所有的发现、出版物、软件和数据都将在项目网站上公开：http://jiayuzhou.github.io/projects/career.This奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Off-Policy Imitation Learning from Observations

• 发表时间: 2021-02
• 期刊: ArXiv
• 作者: Zhuangdi Zhu;Kaixiang Lin;Bo Dai;Jiayu Zhou

MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph.

• DOI: 10.1145/3447548.3467186
• 发表时间: 2021-08
• 期刊: KDD : proceedings. International Conference on Knowledge Discovery & Data Mining

Robust Unsupervised Domain Adaptation from A Corrupted Source

• DOI: 10.1109/icdm54844.2022.00171
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining (ICDM)
• 作者: Shuyang Yu;Zhuangdi Zhu;Boyang Liu;Anil K. Jain;Jiayu Zhou

RUSH: Robust Contrastive Learning via Randomized Smoothing

• DOI: 10.48550/arxiv.2207.05127
• 发表时间: 2022-07
• 期刊: ArXiv
• 作者: Yijiang Pang;Boyang Liu;Jiayu Zhou

Resilient and Communication Efficient Learning for Heterogeneous Federated Systems

• 发表时间: 2022-07
• 期刊: Proceedings of machine learning research
• 作者: Zhuangdi Zhu;Junyuan Hong;S. Drew;Jiayu Zhou