Yongqiang Chen

Orcid: 0000-0003-2485-3529

Affiliations:

The Chinese University of Hong Kong

According to our database¹, Yongqiang Chen authored at least 23 papers between 2021 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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PhD thesis

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Bibliography

2024

Solving the non-submodular network collapse problems via Decision Transformer.

[BibT_eX]

[DOI]

Neural Networks, 2024

UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation.

[BibT_eX]

[DOI]

CoRR, 2024

HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment.

[BibT_eX]

[DOI]

CoRR, 2024

Do CLIPs Always Generalize Better than ImageNet Models?

[BibT_eX]

[DOI]

CoRR, 2024

Discovery of the Hidden World with Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2024

Empowering Graph Invariance Learning with Deep Spurious Infomax.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

How Interpretable Are Interpretable Graph Neural Networks?

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Enhancing Evolving Domain Generalization through Dynamic Latent Representations.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Calibrating and Improving Graph Contrastive Learning.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes.

[BibT_eX]

[DOI]

CoRR, 2023

Towards out-of-distribution generalizable predictions of chemical kinetics properties.

[BibT_eX]

[DOI]

CoRR, 2023

Towards Understanding Feature Learning in Out-of-Distribution Generalization.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Pareto Invariant Risk Minimization.

[BibT_eX]

[DOI]

CoRR, 2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs.

[BibT_eX]

[DOI]

CoRR, 2022

Exact Shape Correspondence via 2D graph convolution.

[BibT_eX]

[DOI]

Barakeel Fanseu Kamhoua

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Improving Graph Representation Learning by Contrastive Regularization.

[BibT_eX]

[DOI]

Barakeel Fanseu Kamhoua

James Cheng

CoRR, 2021

Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Yongqiang Chen

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