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

未来具有国家重要性的应用，如医疗保健、关键基础设施、交通系统和智慧城市，预计将越来越依赖机器学习方法，包括结构化学习、监督学习和强化学习。在许多这些应用中，管理数据的概率分布可能会随着时间和位置而变化，并且数据可能会被错误或恶意代理/传感器损坏。这种模型偏差和数据损坏可能导致显著的性能下降。该项目的目标是探索新的方法来设计学习和推理方法，这些方法对分布不确定性和数据损坏具有鲁棒性。该项目是连接和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医疗保健，交通系统，智能电网和智能城市等社会重要性领域的广泛应用产生重大影响。研究人员正在会议，研讨会和研讨会上共同组织关于强大学习和推理的特别会议，以传播该项目的研究成果，正式确定影响深远的研究方向，确定这一新兴领域的新挑战，刺激原创研究思想的发展，并促进跨学科合作。调查人员致力于扩大代表性不足的少数民族和妇女在计算和工程专业的研究生和本科生中的参与。研究人员正在丰富他们现有的课程，并进一步开发与该项目相关的新课程。该项目有望为鲁棒学习和推理的理论和实践做出新的贡献。正在研究的几个新兴方向包括鲁棒的基于草图的学习、鲁棒的均值估计、机器学习模型的混淆输入的合成、推理时对分布不确定性的鲁棒性以及鲁棒的无模型强化学习。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.