We study the problem of dynamic matching in heterogeneous networks, where agents are subject to compatibility restrictions and stochastic arrival and departure times. In particular, we consider networks with one type of easy-to-match agents and multiple types of hard-to-match agents, each subject to its own compatibility constraints. Such a setting arises in many real-world applications, including kidney exchange programs and carpooling platforms. We introduce a novel approach to modeling dynamic matching by establishing the ordinary differential equation (ODE) model, which offers a new perspective for evaluating various matching algorithms. We study two algorithms, namely the Greedy and Patient Algorithms, where both algorithms prioritize matching compatible hard-to-match agents over easy-to-match agents in heterogeneous networks. Our results demonstrate the trade-off between the conflicting goals of matching agents quickly and optimally, offering insights into the design of real-world dynamic matching systems. We provide simulations and a real-world case study using data from the Organ Procurement and Transplantation Network to validate theoretical predictions.
FlowCast-ODE: Continuous Hourly Weather Forecasting with Dynamic Flow Matching and ODE Solver
ColorfulClouds Technology Co.,Ltd.（ColorfulClouds技术有限公司）；School of Systems Science, Beijing Normal University（北京师范大学系统科学学院）；D-ITET, ETH Zurich（ETH Zurich的D-ITET）
Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries and Denoisers
Department of Mathematics, University of Missouri（密苏里大学数学系）；Halıcıoğlu Data Science Institute, University of California San Diego（加州大学圣地亚哥分校Halıcıoğlu数据科学研究院）
From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems
College of Science, National University of Defense Technology（国防科技大学理学院）；Research Institute of Intelligent Complex Systems, Fudan University（复旦大学智能复杂系统研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Beyond Trajectory Matching: Reflow with Marginal Distribution Alignment
Department of Statistics and Data Science, Tsinghua University（统计与数据科学系，清华大学）；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；Bytedance Inc（字节跳动公司）
Land-then-transport: A Flow Matching-Based Generative Decoder for Wireless Image Transmission
School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology（电气工程与计算机科学系（EECS），皇家理工学院）；College of Information and Communication Engineering, Sungkyunkwan University（信息与通信工程学院，成均馆大学）
Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching
AIMS Research and Innovation Center（AIMS研究与创新中心）；Mila Québec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；SAIT Montreal AI Lab（蒙特利尔SAIT人工智能实验室）
Booth School of Business, University of Chicago（芝加哥大学布斯商学院）；Department of Statistics, University of Michigan（密歇根大学统计系）；Department of Mathematics, University of Maryland, College Park（马里兰大学帕克分校数学系）
Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Stevens Institute of Technology（史蒂文斯理工学院）；The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；South China University of Technology（华南理工大学）；The Simons Institute for the Theory of Computing（西蒙斯理论计算研究所）
Exchangeable deep neural networks for set-to-set matching and learning
ZOZO Research（ZOZO研究院）；The Graduate University for Advanced Studies, SOKENDAI（综合研究大学院大学）；Wakayama University（和歌山大学）；The Institute of Statistical Mathematics（统计数学研究所）
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
Qiushi Academy for Advanced Studies, Zhejiang University（浙江大学求是高等研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；University of Pennsylvania（宾夕法尼亚大学）；Zhejiang Lab（之江实验室）；State Key Lab of CAD&CG, Zhejiang University（浙江大学计算机辅助设计与图形学国家重点实验室）
Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning
School of Computer Science, The University of Sheffield, UK（谢菲尔德大学计算机科学学院，英国）；Cancer Research UK National Biomarker Centre, The University of Manchester, UK（英国曼彻斯特大学癌症研究UK国家生物标志物中心）；School of Chemical, Materials and Biological Engineering, The University of Sheffield, UK（谢菲尔德大学化学、材料和生物工程学院，英国）；Machine Learning group, Centrum Wiskunde & Informatica, Amsterdam, Netherlands（荷兰阿姆斯特丹Centrum Wiskunde & Informatica机器学习组）；Institute for Ecological Economics, Vienna University of Economics and Business, Austria（奥地利维也纳经济与商业大学生态经济研究所）
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Department of Mechanical Engineering, Purdue University（机械工程系，普渡大学）；Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin（航空航天工程与工程力学系，德克萨斯大学奥斯汀分校）；Department of Mechanical and Metallurgical Engineering and Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile（机械与冶金工程系和生物医学工程研究所，智利天主教大学）；Weldon School of Biomedical Engineering, Purdue University（生物医学工程学院，普渡大学）
From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University（虚拟现实技术与系统国家重点实验室，计算机科学与工程学院，北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Center for AI Business Innovation, Department of Management Science and Systems, School of Management, University at Buffalo（人工智能商业创新中心，管理科学与系统系，布法罗大学）
