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Creators/Authors contains: "Chen, Jinghui"

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  1. ; ; ; ; ; (, ACM)
    Free, publicly-accessible full text available November 19, 2026
  2. ; ; ; ; (, Association for Computational Linguistics)
    Full Text Available 
  3. ; (, Transactions on machine learning research)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; (, Association for Computational Linguistics)
    Full Text Available 
  5. ; ; ; ; (, The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, Proceedings of the 41st International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; (, Annual Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; (, Annual Conference on Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; (, Proceedings of the 41st International Conference on Machine Learning (ICML))
    This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem. In this formulation, we first maximize the second term of GSD by introducing perturbations to the graph structure based on Laplacian distance and then minimize the overall loss of the GSD. By solving the min-max optimization problem, we derive a new variant of the Graph Diffusion Convolution (GDC) architecture, called Graph Adversarial Diffusion Convolution (GADC). GADC differs from GDC by incorporateing an additional term that enhances robustness against adversarial attacks on the graph structure and noise in node features. Moreover, GADC improves the performance of GDC on heterophilic graphs. Extensive experiments demonstrate the effectiveness of GADC across various datasets. Code is available at https://github.com/SongtaoLiu0823/GADC. 
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    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; (, Proceedings of the 41st International Conference on Machine Learning (ICML))
    This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem. In this formulation, we first maximize the second term of GSD by introducing perturbations to the graph structure based on Laplacian distance and then minimize the overall loss of the GSD. By solving the min-max optimization problem, we derive a new variant of the Graph Diffusion Convolution (GDC) architecture, called Graph Adversarial Diffusion Convolution (GADC). GADC differs from GDC by incorporating an additional term that enhances robustness against adversarial attacks on the graph structure and noise in node features. Moreover, GADC improves the performance of GDC on heterophilic graphs. Extensive experiments demonstrate the effectiveness of GADC across various datasets. Code is available at https://github.com/SongtaoLiu0823/GADC. 
    more » « less
    Accepted Manuscript Full Text Available