1.9k citations · 2.9k across the 29 of their papers we have counts for
31 papers
RDBLearn: Simple In-Context Prediction Over Relational Databases
Yanlin Zhang, Linjie Xu, Quan Gan +2
Recent advances in tabular in-context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per-task t…
No Need to Train Your RDB Foundation Model
Linjie Xu, Yanlin Zhang, Quan Gan +2
Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes. But since the space of potential targe…
Diffusion-Driven High-Dimensional Variable Selection
Minjie Wang, Xiaotong Shen, Wei Pan
Variable selection for high-dimensional, highly correlated data has long been a challenging problem, often yielding unstable and unreliable models. We propose a resample-aggregate…
Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Ning Li, Kounianhua Du, Han Zhang +4
Relational databases (RDBs) have become the industry standard for storing massive and heterogeneous data. However, despite the widespread use of RDBs across various fields, the inh…
Griffin: Towards a Graph-Centric Relational Database Foundation Model
Yanbo Wang, Xiyuan Wang, Quan Gan +4
We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller models focused on single RDB tasks, Gri…
DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs
Jiahui Liu, Zhenkun Cai, Zhiyong Chen +1
Attention Graph Neural Networks (AT-GNNs), such as GAT and Graph Transformer, have demonstrated superior performance compared to other GNNs. However, existing GNN systems struggle…
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
Yanlin Zhang, Ning Li, Quan Gan +3
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (…
Heta: Distributed Training of Heterogeneous Graph Neural Networks
Yuchen Zhong, Junwei Su, Chuan Wu +1
Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning performance in various…
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
Renjie Liu, Yichuan Wang, Xiao Yan +5
Graph neural networks (GNNs) are machine learning models specialized for graph data and widely used in many applications. To train GNNs on large graphs that exceed CPU memory, seve…
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Minjie Wang, Quan Gan, David Wipf +17
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably…
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
Amber Yijia Zheng, Tong He, Yixuan Qiu +2
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy function serves as input features to an upper-level objective of interest. These optim…
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs
Yuchen Zhong, Guangming Sheng, Tianzuo Qin +3
Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing deep graph learning frameworks assume pre-stored static graphs and do not support trainin…
Causal Discovery with Generalized Linear Models through Peeling Algorithms
Minjie Wang, Xiaotong Shen, Wei Pan
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous…
MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
Haitian Jiang, Renjie Liu, Zengfeng Huang +5
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
Kezhao Huang, Haitian Jiang, Minjie Wang +7
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU. Due to limited GPU memory, expensive d…
DGI: Easy and Efficient Inference for GNNs
Peiqi Yin, Xiao Yan, Jinjing Zhou +5
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopte…
BiFeat: Supercharge GNN Training via Graph Feature Quantization
Yuxin Ma, Ping Gong, Jun Yi +4
Graph Neural Networks (GNNs) is a promising approach for applications with nonEuclidean data. However, training GNNs on large scale graphs with hundreds of millions nodes is both r…
CEP3: Community Event Prediction with Neural Point Process on Graph
Xuhong Wang, Sirui Chen, Yixuan He +4
Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represen…
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles
Tianyi Yao, Minjie Wang, Genevera I. Allen
Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of…
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity
Minjie Wang, Genevera I. Allen
In neuroscience, researchers seek to uncover the connectivity of neurons from large-scale neural recordings or imaging; often people employ graphical model selection and estimation…
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Da Zheng, Chao Ma, Minjie Wang +6
Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection,…
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Yuwei Hu, Zihao Ye, Minjie Wang +6
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is a…
Sparse Regression for Extreme Values
Andersen Chang, Minjie Wang, Genevera Allen
We study the problem of selecting features associated with extreme values in high dimensional linear regression. Normally, in linear modeling problems, the presence of abnormal ext…