30 citations · 213 across the 50 of their papers we have counts for
28 papers · 1 filter
Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes
Fred Xu, Thomas Markovich, Florence Regol +1
Node-level graph anomaly detection (GAD) identifies nodes whose attributes and interactions deviate from dominant graph regularities. Existing GAD models encode normality and anoma…
Improving Function Space Flow Matching with Kernel Optimal Transport
Fred Xu, Thomas Markovich, Barbora Barancikova +1
Generative models for function-valued data, such as time series and solutions of partial differential equations, must learn distributions over infinite-dimensional spaces. Function…
Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation
Xiaoda Wang, Kaiqiao Han, Yuhao Xu +4
Cardiovascular disease (CVD) is a leading cause of mortality worldwide. Electrocardiograms (ECGs) are the most widely used non-invasive tool for cardiac assessment, yet large, well…
Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting
Xiaoda Wang, Yuji Zhao, Kaiqiao Han +8
Parkinson's disease (PD) shows heterogeneous, evolving brain-morphometry patterns. Modeling these longitudinal trajectories enables mechanistic insight, treatment development, and…
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion
Fred Xu, Song Jiang, Zijie Huang +4
Taxonomy Expansion, which models complex concepts and their relations, can be formulated as a set representation learning task. The generalization of set, fuzzy set, incorporates u…
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation
Yanna Ding, Zijie Huang, Xiao Shou +3
Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural…
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Fang Sun, Zijie Huang, Haixin Wang +4
Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over ex…
Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling
Zijie Huang, Wanjia Zhao, Jingdong Gao +6
Learning complex physical dynamics purely from data is challenging due to the intrinsic properties of systems to be satisfied. Incorporating physics-informed priors, such as in Ham…
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
Haixin Wang, Yadi Cao, Zijie Huang +13
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental conc…
Mixture of In-Context Prompters for Tabular PFNs
Derek Xu, Olcay Cirit, Reza Asadi +2
Recent benchmarks found In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular l…
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Kaiqiao Han, Yi Yang, Zijie Huang +8
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. Ho…
Hierarchical Attention Models for Multi-Relational Graphs
Roshni G. Iyer, Wei Wang, Yizhou Sun
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relationa…
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
Zijie Huang, Jeehyun Hwang, Junkai Zhang +6
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the C…
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Zijie Huang, Wanjia Zhao, Jingdong Gao +6
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven appro…
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Yanqiao Zhu, Jeehyun Hwang, Keir Adams +10
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are e…
Unveiling Invariances via Neural Network Pruning
Derek Xu, Yizhou Sun, Wei Wang
Invariance describes transformations that do not alter data's underlying semantics. Neural networks that preserve natural invariance capture good inductive biases and achieve super…
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
Zijie Huang, Yizhou Sun, Wei Wang
Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built…
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems
Song Jiang, Zijie Huang, Xiao Luo +1
Multi-agent dynamical systems refer to scenarios where multiple units interact with each other and evolve collectively over time. To make informed decisions in multi-agent dynamica…
Bi-Level Attention Graph Neural Networks
Roshni G. Iyer, Wei Wang, Yizhou Sun
Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs). However, GNNs handling heterogeneous graph…
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Danfeng Guo, Zijie Huang, Junheng Hao +3
Pandemic(epidemic) modeling, aiming at disease spreading analysis, has always been a popular research topic especially following the outbreak of COVID-19 in 2019. Some representati…
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search
Yunsheng Bai, Derek Xu, Yizhou Sun +1
Recent advances have shown the success of using reinforcement learning and search to solve NP-hard graph-related tasks, such as Traveling Salesman Optimization, Graph Edit Distance…
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
Zijie Huang, Yizhou Sun, Wei Wang
Many real-world systems, such as moving planets, can be considered as multi-agent dynamic systems, where objects interact with each other and co-evolve along with the time. Such dy…
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
Ziheng Duan, Haoyan Xu, Yueyang Wang +5
With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC technique…
GLSearch: Maximum Common Subgraph Detection via Learning to Search
Yunsheng Bai, Derek Xu, Yizhou Sun +1
Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in drug synthesis, malware detection, cloud computing, etc. However, MCS comput…