Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian（蒙古智能信息处理技术国家地方联合工程研究中心）
Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the performance of link prediction. To overcome this limitation, we introduce a novel method that maps factor tensors onto a unified smooth Lie group manifold to make the distribution of factor tensors approximating homogeneous in tensor decomposition. We provide the theoretical proof of our motivation that homogeneous tensors are more effective than heterogeneous tensors in tensor fusion and approximating the target for tensor decomposition based TKGE methods. The proposed method can be directly integrated into existing tensor decomposition based TKGE methods without introducing extra parameters. Extensive experiments demonstrate the effectiveness of our method in mitigating the heterogeneity and in enhancing the tensor decomposition based TKGE models.
Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
University of California, Riverside（加州大学河滨分校）；Indian Institute of Science（印度科学研究院）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；DEVCOM Army Research Laboratory（陆军研究实验室）
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
MetaMind Innovation P.C.（MetaMind创新有限公司）；University of Western Macedonia（西马其顿大学）；K3Y Ltd（K3Y有限公司）；Democritus University of Thrace（色雷斯德谟克利特大学）；Kingston University London（伦敦金斯顿大学）
The Horcrux: Mechanistically Interpretable Task Decomposition for Detecting and Mitigating Reward Hacking in Embodied AI Systems
Berkeley AI Safety Initiative (BASIS) UC Berkeley（伯克利人工智能安全计划（BASIS）伯克利大学）；Department of Electrical and Computer Engineering Johns Hopkins University（电气与计算机工程系约翰霍普金斯大学）
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学网络空间学院）；New H3C Technologies Co., Ltd.（新华三技术有限公司）；Computer Science and Engineering, University of California, Santa Cruz（加州大学圣克鲁兹分校计算机科学与工程系）；Department of Statistics and Actuarial Science, The University of Hong Kong（香港大学统计与精算科学系）
Improving Group Fairness in Tensor Completion via Imbalance Mitigating Entity Augmentation
Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, USA（计算机科学与工程系，加州大学河滨分校）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA（计算与数据科学学院，伊利诺伊大学厄巴纳-香槟分校）
H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；School of Computer Science and Technology, Heilongjiang University（黑龙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Statistics, Renmin University of China（中国人民大学统计学院）；Zhongguancun Laboratory, China（中关村实验室）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；WeBank Co., Ltd, Shenzhen, China（WeBank有限公司，深圳，中国）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics（南京航空航天大学计算机科学与技术学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics & Astronautics（南京航空航天大学工信部模式分析与机器智能重点实验室）
FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；National Engineering Research Center of Disaster Backup and Recovery, Beijing University of Posts and Telecommunications（北京邮电大学国家灾备工程技术研究中心）；JIUTIAN Research, China Mobile（中国移动九天研究院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）；University of Leicester（莱斯特大学）
