3.7k citations · 3.8k across the 13 of their papers we have counts for
15 papers
Differentially Private Relational Learning with Entity-level Privacy Guarantees
Yinan Huang, Haoteng Yin, Eli Chien +2
Learning with relational and network-structured data is increasingly vital in sensitive domains where protecting the privacy of individual entities is paramount. Differential Priva…
Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awareness
Rongzhe Wei, Peizhi Niu, Hans Hao-Hsun Hsu +9
Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs). However, existing approaches predominantly focus on the explicit removal of i…
Privately Learning from Graphs with Applications in Fine-tuning Large Language Models
Haoteng Yin, Rongzhe Wei, Eli Chien +1
Graphs offer unique insights into relationships between entities, complementing data modalities like text and images and enabling AI models to extend their capabilities beyond trad…
SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned Latents
Wei Xiang, Haoteng Yin, He Wang +1
Pedestrian trajectory prediction is the key technology in many applications for providing insights into human behavior and anticipating human future motions. Most existing empirica…
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures
Tianyi Zhang, Haoteng Yin, Rongzhe Wei +2
Graph neural networks (GNNs) have shown great potential in learning on graphs, but they are known to perform sub-optimally on link prediction tasks. Existing GNNs are primarily des…
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Rongzhe Wei, Eleonora Kreačić, Haoyu Wang +4
Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical ev…
OCTAL: Graph Representation Learning for LTL Model Checking
Prasita Mukherjee, Haoteng Yin
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, suffer from the sta…
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
Haoteng Yin, Muhan Zhang, Jianguo Wang +1
Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and gen…
OCTAL: Graph Representation Learning for LTL Model Checking
Prasita Mukherjee, Haoteng Yin, Susheel Suresh +1
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from t…
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective
Rongzhe Wei, Haoteng Yin, Junteng Jia +2
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured dat…
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Haorui Wang, Haoteng Yin, Muhan Zhang +1
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif…
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Haoteng Yin, Muhan Zhang, Yanbang Wang +2
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and h…
Revisiting graph neural networks and distance encoding from a practical view
Haoteng Yin, Yanbang Wang, Pan Li
Graph neural networks (GNNs) are widely used in the applications based on graph structured data, such as node classification and link prediction. However, GNNs are often used as a…
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling
Bing Yu, Haoteng Yin, Zhanxing Zhu
The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is…
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Bing Yu, Haoteng Yin, Zhanxing Zhu
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy…