most citedGenerative Risk Minimization for Out-of-Distribution Generalization on Graphs

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks

Song Wang, Zhenyu Lei, Zhen Tan +4

Nearly one in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder, underscoring the urgency of…

cs.CL2025

Learning from Diverse Reasoning Paths with Routing and Collaboration

Zhenyu Lei, Zhen Tan, Song Wang +4

Advances in large language models (LLMs) significantly enhance reasoning capabilities but their deployment is restricted in resource-constrained scenarios. Knowledge distillation a…

cs.AI2025

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction

Song Wang, Zhen Tan, Zihan Chen +3

Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existin…

cs.AI2025

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning

Zihan Chen, Song Wang, Zhen Tan +2

In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle diverse tasks by incorporating multiple input-output examples, known as demonstrations, into the input of…

cs.LG2025

Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations

Zhen Tan, Song Wang, Yifan Li +4

Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how the inputs lead to the predicti…

cs.LG20251 cited

Generative Risk Minimization for Out-of-Distribution Generalization on Graphs

Song Wang, Zhen Tan, Yaochen Zhu +2

Out-of-distribution (OOD) generalization on graphs aims at dealing with scenarios where the test graph distribution differs from the training graph distributions. Compared to i.i.d…