2.3k citations · 3.8k across the 29 of their papers we have counts for
19 papers · 1 filter
TimeGraphs: Graph-based Temporal Reasoning
Paridhi Maheshwari, Hongyu Ren, Yanan Wang +2
Many real-world systems exhibit temporal, dynamic behaviors, which are captured as time series of complex agent interactions. To perform temporal reasoning, current methods primari…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
Enabling tabular deep learning when with an auxiliary knowledge graph
Camilo Ruiz, Hongyu Ren, Kexin Huang +1
Machine learning models exhibit strong performance on datasets with abundant labeled samples. However, for tabular datasets with extremely high -dimensional features but limited…
PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang, Hongyu Ren, Peng Chen +4
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters. While…
Few-shot Relational Reasoning via Connection Subgraph Pretraining
Qian Huang, Hongyu Ren, Jure Leskovec
Few-shot knowledge graph (KG) completion task aims to perform inductive reasoning over the KG: given only a few support triplets of a new relation (e.g., (chop,,…
TripleE: Easy Domain Generalization via Episodic Replay
Xiaomeng Li, Hongyu Ren, Huifeng Yao +1
Learning how to generalize the model to unseen domains is an important area of research. In this paper, we propose TripleE, and the main idea is to encourage the network to focus o…
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai +4
Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs:…
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Yushi Bai, Rex Ying, Hongyu Ren +1
Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they in…
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.…
Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren, Hanjun Dai, Zihang Dai +4
Transformers provide a class of expressive architectures that are extremely effective for sequence modeling. However, the key limitation of transformers is their quadratic memory a…
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren +3
Enabling effective and efficient machine learning (ML) over large-scale graph data (e.g., graphs with billions of edges) can have a great impact on both industrial and scientific a…
Graph Information Bottleneck
Tailin Wu, Hongyu Ren, Pan Li +1
Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an…
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Hongyu Ren, Yuke Zhu, Jure Leskovec +2
Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pull…
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik +5
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML…
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Hongyu Ren, Weihua Hu, Jure Leskovec
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to…
Relational Message Passing for Knowledge Graph Completion
Hongwei Wang, Hongyu Ren, Jure Leskovec
Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph…
Bias and Generalization in Deep Generative Models: An Empirical Study
Shengjia Zhao, Hongyu Ren, Arianna Yuan +3
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Despite recent empirical success, the inductive bias of deep generative models i…
Multi-Agent Generative Adversarial Imitation Learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh +1
Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in mu…
Adversarial Constraint Learning for Structured Prediction
Hongyu Ren, Russell Stewart, Jiaming Song +2
Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We…