Network regularization is an effective tool for incorporating structural prior knowledge to learn coherent models over networks, and has yielded provably accurate estimates in applications ranging from spatial economics to neuroimaging studies. Recently, there has been an increasing interest in extending network regularization to the spatio-temporal case to accommodate the evolution of networks. However, in both static and spatio-temporal cases, missing or corrupted edge weights can compromise the ability of network regularization to discover desired solutions. To address these gaps, we propose a novel approach---{\it discrepancy-aware network regularization} (DANR)---that is robust to inadequate regularizations and effectively captures model evolution and structural changes over spatio-temporal networks. We develop a distributed and scalable algorithm based on the alternating direction method of multipliers (ADMM) to solve the proposed problem with guaranteed convergence to global optimum solutions. Experimental results on both synthetic and real-world networks demonstrate that our approach achieves improved performance on various tasks, and enables interpretation of model changes in evolving networks.
Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning
School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；School of Mechanical Engineering, Huazhong University of Science and Technology（华中科技大学机械工程学院）；Xihua University（西华大学）；School of Computation, Information and Technology, Technische Universität München（慕尼黑工业大学计算、信息与技术学院）；China Mobile Communications Group Chongqing Co., Ltd.（中国移动通信集团重庆有限公司）
Efficient Cross-Validation for Semi-Supervised Learning
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Inception Institute of Artificial Intelligence (IIAI)（阿布扎比人工智能孵化研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；National Engineering Research Center for Information Security（国家信息安全工程技术研究中心）；National Engineering Laboratory for Information Security Technology（国家信息安全技术工程实验室）
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
University of Florida (UF)（佛罗里达大学）；Torch Technologies, LLC（Torch科技有限责任公司）；McKnight Brain Institute（麦克奈特脑研究所）；College of Public Health and Health Professions（公共卫生与健康职业学院）；College of Medicine（医学院）；United States Air Force Research Laboratory（美国空军研究实验室）；Herbert Wertheim College of Engineering（赫伯特·韦特海姆工程学院）；Department of Biomedical Engineering（生物医学工程系）；Department of Clinical and Health Psychology（临床与健康心理学系）；Department of Neuroscience（神经科学系）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Computer Information and Science and Engineering（计算机信息与工程系）
