Yatao Bian

papers

707

total citations

papers (14)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

NEURIPS 2022arXiv

200

citations

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

NEURIPS 2023arXiv

192

citations

Fairness-guided Few-shot Prompting for Large Language Models

NEURIPS 2023arXiv

citations

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

NEURIPS 2023arXiv

citations

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

NEURIPS 2022arXiv

citations

Understanding and Improving Feature Learning for Out-of-Distribution Generalization

NEURIPS 2023arXiv

citations

EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model

ICLR 2024

citations

InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization

ICLR 2025arXiv

citations

Not All Low-Pass Filters are Robust in Graph Convolutional Networks

NEURIPS 2021

citations

Yatao Bian

papers (14)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Fairness-guided Few-shot Prompting for Large Language Models

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Understanding and Improving Feature Learning for Out-of-Distribution Generalization

Self-Supervised Graph Transformer on Large-Scale Molecular Data

Learning Invariant Molecular Representation in Latent Discrete Space

How Interpretable Are Interpretable Graph Neural Networks?

Learning Neural Set Functions Under the Optimal Subset Oracle

Hierarchical Graph Tokenization for Molecule-Language Alignment

EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model

InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization

Not All Low-Pass Filters are Robust in Graph Convolutional Networks

papers (14)

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations

Fairness-guided Few-shot Prompting for Large Language Models

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Understanding and Improving Feature Learning for Out-of-Distribution Generalization

Self-Supervised Graph Transformer on Large-Scale Molecular Data

Learning Invariant Molecular Representation in Latent Discrete Space

How Interpretable Are Interpretable Graph Neural Networks?

Learning Neural Set Functions Under the Optimal Subset Oracle

Hierarchical Graph Tokenization for Molecule-Language Alignment

EBMDock: Neural Probabilistic Protein-Protein Docking via a Differentiable Energy Model

InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization

Not All Low-Pass Filters are Robust in Graph Convolutional Networks