Qitian Wu

papers

990

total citations

papers (16)

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

NEURIPS 2022arXiv

344

citations

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

NEURIPS 2021arXiv

278

citations

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

NEURIPS 2023arXiv

192

citations

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

NEURIPS 2022arXiv

citations

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

ICLR 2025arXiv

citations

Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators

NEURIPS 2021arXiv

citations

How Graph Neural Networks Learn: Lessons from Training Dynamics

ICML 2024arXiv

citations

Learning Divergence Fields for Shift-Robust Graph Representations

ICML 2024arXiv

citations

Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning

ICML 2025

citations

Graph Out-of-Distribution Detection Goes Neighborhood Shaping

ICML 2024

citations

TDeLTA: A Light-Weight and Robust Table Detection Method Based on Learning Text Arrangement

AAAI 2024arXiv

citations

Learning Substructure Invariance for Out-of-Distribution Molecular Representations

NEURIPS 2022

citations

GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs

NEURIPS 2022

citations

Qitian Wu

papers (16)

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators

How Graph Neural Networks Learn: Lessons from Training Dynamics

Learning Divergence Fields for Shift-Robust Graph Representations

Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning

Graph Out-of-Distribution Detection Goes Neighborhood Shaping

TDeLTA: A Light-Weight and Robust Table Detection Method Based on Learning Text Arrangement

Learning Substructure Invariance for Out-of-Distribution Molecular Representations

GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs

papers (16)

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators

How Graph Neural Networks Learn: Lessons from Training Dynamics

Learning Divergence Fields for Shift-Robust Graph Representations

Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning

Graph Out-of-Distribution Detection Goes Neighborhood Shaping

TDeLTA: A Light-Weight and Robust Table Detection Method Based on Learning Text Arrangement

Learning Substructure Invariance for Out-of-Distribution Molecular Representations

GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs