Collaborative Research: CIF: Medium: Emerging Directions in Robust Learning and Inference

协作研究：CIF：媒介：稳健学习和推理的新兴方向

• 批准号: 2106560
• 负责人: Shaofeng Zou
• 金额: $ 37.47万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106560&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; Emerging

Future applications of national importance, such as healthcare, critical infrastructure, transportation systems, and smart cities, are expected to increasingly rely on machine-learning methods, including structured learning, supervised learning, and reinforcement learning. In many of these applications, the probabilistic distribution governing the data may undergo variations with time and location, and data could be corrupted by faulty or malicious agents/sensors. Such model deviation and data corruption could result in significant performance degradation. The goal in this project is to explore new ways to design learning and inference methods that are robust to distributional uncertainty and data corruption. This project is bridging and further advancing research in areas of statistical learning, optimization, control theory, network science, reinforcement learning, statistical signal processing and information theory. The methods developed are likely to have significant impact on a wide range of applications in areas of societal importance such as healthcare, transportation systems, smart grids, and smart cities. The investigators are co-organizing special sessions at conferences, workshops and symposia on robust learning and inference to disseminate the research outcomes of this project, formalize far-reaching research directions, identify new challenges in this emerging area, stimulate the development of original research ideas, and foster interdisciplinary collaborations. The investigators are committed to broadening participation of under-represented minorities and women both among the graduate and undergraduate students in computing and engineering. The investigators are enriching their current courses and further developing new courses on topics related to this project.This project is expected to make new contributions to the theory and practice of robust learning and inference. Several emerging directions are being investigated, including robust sketch-based learning, robust mean estimation, synthesis of confusing inputs to machine-learning models, robustness to distributional uncertainty at inference time, and robust model-free reinforcement learning.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.

预计未来国家重要性的应用，例如医疗保健，关键基础设施，运输系统和智能城市，将越来越多地依赖机器学习方法，包括结构化学习，监督学习和加强学习。在许多这些应用程序中，控制数据的概率分布可能会随时间和位置的变化而发生变化，并且数据可能会因错误或恶意代理/传感器而损坏。这种模型偏差和数据损坏可能会导致大量的性能下降。该项目的目标是探索设计学习和推理方法的新方法，这些方法对分配不确定性和数据损坏是可靠的。该项目正在桥接，并进一步推进统计学习，优化，控制理论，网络科学，强化学习，统计信号处理和信息理论领域的研究。开发的方法可能会对社会重要性领域（例如医疗保健，运输系统，智能网格和智能城市）的广泛应用产生重大影响。研究人员正在共同组织会议，讲习班和研讨会，以进行强大的学习和推理，以传播该项目的研究成果，正式化了深远的研究方向，确定了这个散发性领域的新挑战，刺激了原始研究思想的发展，并促进了学科跨学科的协作。调查人员致力于扩大代表性不足的少数民族和妇女的参与，包括研究生和本科生在计算和工程领域的参与。调查人员正在丰富目前的课程，并进一步开发有关该项目有关的主题的新课程。该项目有望为强有力的学习和推理做出新的贡献。正在研究几个新兴方向，包括基于强大的素描学习，稳健的平均值估计，对机器学习模型的混淆的综合，对推理时间的鲁棒性对分配不确定性的鲁棒性以及坚固的模型强化学习。这项奖项反映了NSF的法定任务，并通过评估智能委员会进行了评估和广泛的影响。

项目成果

Data-Driven Robust Multi-Agent Reinforcement Learning

• DOI: 10.1109/mlsp55214.2022.9943500
• 发表时间: 2022-08
• 期刊: 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)
• 作者: Yudan Wang;Yue Wang;Yi Zhou;Alvaro Velasquez;Shaofeng Zou

Policy Gradient Method For Robust Reinforcement Learning

• DOI: 10.48550/arxiv.2205.07344
• 发表时间: 2022-05
• 作者: Yue Wang;Shaofeng Zou

Robust Average-Reward Markov Decision Processes

鲁棒平均奖励马尔可夫决策过程

• DOI: 10.1609/aaai.v37i12.26775
• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Wang, Yue;Velasquez, Alvaro;Atia, George;Prater-Bennette, Ashley;Zou, Shaofeng
• 通讯作者: Zou, Shaofeng

Online Robust Reinforcement Learning with Model Uncertainty

• 发表时间: 2021-09
• 期刊: ArXiv
• 作者: Yue Wang;Shaofeng Zou

Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis

• 发表时间: 2021-09
• 作者: Ziyi Chen;Yi Zhou;Rongrong Chen;Shaofeng Zou