Collaborative Research: CIF: Small: Nonasymptotic Analysis for Stochastic Networks and Systems: Foundations and Applications

合作研究：CIF：小型：随机网络和系统的非渐近分析：基础和应用

• 批准号: 2207548
• 负责人: Lei Ying
• 金额: $ 30万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207548&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Small; Nonasymptotic

The confluence of cloud computing, machine learning/artificial intelligence and Internet-of-things/sensor technology is transforming society in unprecedented ways, and leading to innovations in autonomous systems, healthcare, bioinformatics, social networks, online and in-store retail industry, and education. Breakthrough developments in these widely disparate fields use machine learning and cloud computing with millions of servers to aid data-driven decision-making using terabytes of data, some in real-time and some offline. At the heart of these large-scale machine-learning and cloud-computing applications are stochastic dynamical systems of enormous scale. Analyzing and optimizing such systems are often difficult because of the size and the unknown statistical description of the underlying randomness in such systems. This project is aimed at understanding the performance of large stochastic systems by developing a new analytical method that synthesizes tools from probability, machine learning, and stochastic networks, and will lead to new advances in the design of fast and more efficient computing systems for training large-scale machine-learning models, while yielding new fundamental insights into deep reinforcement-learning algorithms. The project will contribute to education and workforce development by integrating the theories and algorithms into the graduate-level courses and by involving undergraduate and students from underrepresented groups in the research. This project develops a new analytical method for obtaining non-asymptotic bounds using Lyapunov drift analysis. The method combines drift analysis with ideas from Stein's method, dimensionality reduction from state-space collapse and properties of reproducing kernel Hilbert spaces, as appropriate. The project leverages three key ideas to advance the state-of-the-art: Stein's method to choose appropriate Lyapunov functions to study mean-field limits, identifying lower-order models using the notion of state-space collapse, and using moment-generating functions or characteristic functions as test functions to obtain higher-moment bounds on the performance of stochastic systems. During the course of this project, the method is applied to two applications: (i) robust and ultra-low latency computing networks for supporting complex machine-learning jobs with concurrent and dependent tasks, which are processed in heterogeneous server farms; and (ii) deep reinforcement-learning for deriving new performance bounds for neural temporal-difference learning and for the Actor-Critic algorithms.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.

云计算、机器学习/人工智能和物联网/传感器技术的融合正在以前所未有的方式改变社会，并导致自主系统、医疗保健、生物信息学、社交网络、在线和店内零售行业的创新，和教育。这些截然不同的领域的突破性发展利用机器学习和云计算以及数百万台服务器来帮助使用数 ​​TB 的数据（有些是实时的，有些是离线的）进行数据驱动的决策。这些大规模机器学习和云计算应用程序的核心是大规模的随机动力系统。由于此类系统的规模和潜在随机性的未知统计描述，分析和优化此类系统通常很困难。该项目旨在通过开发一种新的分析方法来了解大型随机系统的性能，该方法综合了概率、机器学习和随机网络的工具，并将带来快速、更高效的计算系统设计的新进展，用于训练大型随机系统规模机器学习模型，同时产生对深度强化学习算法的新的基本见解。该项目将通过将理论和算法整合到研究生课程中，并让本科生和来自弱势群体的学生参与研究，为教育和劳动力发展做出贡献。该项目开发了一种新的分析方法，用于使用李亚普诺夫漂移分析获得非渐近边界。该方法将漂移分析与 Stein 方法的思想、状态空间崩溃的降维以及再生核希尔伯特空间的属性（适当时）相结合。该项目利用三个关键思想来推进最先进的技术：斯坦因选择适当的李亚普诺夫函数来研究平均场极限的方法，使用状态空间崩溃的概念识别低阶模型，以及使用矩生成函数或特征函数作为测试函数，以获得随机系统性能的更高矩界限。 在该项目过程中，该方法应用于两个应用程序：（i）强大且超低延迟的计算网络，用于支持具有并发和依赖任务的复杂机器学习作业，这些任务在异构服务器群中进行处理； (ii) 深度强化学习，用于为神经时间差异学习和 Actor-Critic 算法推导新的性能界限。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Learning While Scheduling in Multi-Server Systems With Unknown Statistics: MaxWeight with Discounted UCB

• 发表时间: 2022-09
• 作者: Zixi Yang;R. Srikant;Lei Ying