7 citations · 9 across the 21 of their papers we have counts for
11 papers · 1 filter
Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations
Guanghui Min, Liang Wu, Mingjia Shi +4
Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing pr…
MolEdit: Knowledge Editing for Multimodal Molecule Language Models
Zhenyu Lei, Patrick Soga, Yaochen Zhu +3
Understanding and continuously refining multimodal molecular knowledge is crucial for advancing biomedicine, chemistry, and materials science. Molecule language models (MoLMs) have…
Energy-Based Models for Predicting Mutational Effects on Proteins
Patrick Soga, Zhenyu Lei, Yinhan He +2
Predicting changes in binding free energy () is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have o…
Edge Prompt Tuning for Graph Neural Networks
Xingbo Fu, Yinhan He, Jundong Li
Pre-training powerful Graph Neural Networks (GNNs) with unlabeled graph data in a self-supervised manner has emerged as a prominent technique in recent years. However, inevitable o…
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Yushun Dong, Patrick Soga, Yinhan He +2
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggreg…
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
Yinhan He, Wendy Zheng, Yaochen Zhu +5
Graph Neural Networks (GNNs) have been widely deployed in various real-world applications. However, most GNNs are black-box models that lack explanations. One strategy to explain G…
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
Yinhan He, Zaiyi Zheng, Patrick Soga +3
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs,…
Causal Inference with Latent Variables: Recent Advances and Future Prospectives
Yaochen Zhu, Yinhan He, Jing Ma +3
Causality lays the foundation for the trajectory of our world. Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a c…
Spectral Greedy Coresets for Graph Neural Networks
Mucong Ding, Yinhan He, Jundong Li +1
The ubiquity of large-scale graphs in node-classification tasks significantly hinders the real-world applications of Graph Neural Networks (GNNs). Node sampling, graph coarsening,…
Safety in Graph Machine Learning: Threats and Safeguards
Song Wang, Yushun Dong, Binchi Zhang +7
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have b…