2.3k citations · 5k across the 24 of their papers we have counts for
24 papers
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
Yanbo Wang, Jiaxuan You, Chuan Shi +1
Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are…
Representation Learning for Frequent Subgraph Mining
Rex Ying, Tianyu Fu, Andrew Wang +3
Identifying frequent subgraphs, also called network motifs, is crucial in analyzing and predicting properties of real-world networks. However, finding large commonly-occurring moti…
Relational Deep Learning: Graph Representation Learning on Relational Databases
Matthias Fey, Weihua Hu, Kexin Huang +6
Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations.…
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks
Kaidi Cao, Jiaxuan You, Jiaju Liu +1
AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. How…
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Kaidi Cao, Jiaxuan You, Jure Leskovec
A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point's labels from other tasks…
Efficient Automatic Machine Learning via Design Graphs
Shirley Wu, Jiaxuan You, Jure Leskovec +1
Despite the success of automated machine learning (AutoML), which aims to find the best design, including the architecture of deep networks and hyper-parameters, conventional AutoM…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
ROLAND: Graph Learning Framework for Dynamic Graphs
Jiaxuan You, Tianyu Du, Jure Leskovec
Graph Neural Networks (GNNs) have been successfully applied to many real-world static graphs. However, the success of static graphs has not fully translated to dynamic graphs due t…
AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
Tim Poštuvan, Jiaxuan You, Mohammadreza Banaei +2
One of the most important factors that contribute to the success of a machine learning model is a good training objective. Training objective crucially influences the model's perfo…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Identity-aware Graph Neural Networks
Jiaxuan You, Jonathan Gomes-Selman, Rex Ying +1
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Wei…
Design Space for Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing a…
Handling Missing Data with Graph Representation Learning
Jiaxuan You, Xiaobai Ma, Daisy Yi Ding +2
Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and…
Inductive Learning on Commonsense Knowledge Graph Completion
Bin Wang, Guangtao Wang, Jing Huang +3
Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on t…
Graph Structure of Neural Networks
Jiaxuan You, Jure Leskovec, Kaiming He +1
Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between…
Neural Subgraph Matching
Rex, Ying, Zhaoyu Lou +4
Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph. Despite being an NP-complete problem, the subgraph matc…
G2SAT: Learning to Generate SAT Formulas
Jiaxuan You, Haoze Wu, Clark Barrett +2
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, a…
Position-aware Graph Neural Networks
Jiaxuan You, Rex Ying, Jure Leskovec
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (G…
Redundancy-Free Computation Graphs for Graph Neural Networks
Zhihao Jia, Sina Lin, Rex Ying +3
Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph. However, because common neighbors are shared between different no…
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Jiaxuan You, Yichen Wang, Aditya Pal +3
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches of…
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying, Dylan Bourgeois, Jiaxuan You +2
Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural message…
Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying, Jiaxuan You, Christopher Morris +3
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art res…
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Rex Ying +2
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This i…
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Jiaxuan You, Rex Ying, Xiang Ren +2
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then effic…