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University of Groningen
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Andrés Tello
Dataset Management
Supervisors:
Huy Truong,
Andrés Tello
Date: 2025-08-25
Type: bi
Description:
Have you ever managed a large dataset? This project provides an opportunity to handle a dataset with over 8,000 downloads each month. You will reorganize the dataset by task, document it thoroughly, and create a user-friendly interface and leaderboard. The project also involves working with HPC clusters, Hugging Face libraries, and GitHub Pages for documentation. Basic Python skills and familiarity with Linux commands are required.
Explaining Graph Neural Networks
Supervisors:
Andrés Tello
Date: 2025-02-01
Type: colloquium
Description:
References:
Wang, X., & Shen, H. W. GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries. In The Twelfth International Conference on Learning Representations. Müller, P., Faber, L., Martinkus, K., & Wattenhofer, R. (2024). GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Lu, S., Mills, K. G., He, J., Liu, B., & Niu, D. (2024). GOAt: Explaining graph neural networks via graph output attribution. arXiv preprint arXiv:2401.14578. Wang, X., & Shen, H. W. (2022). Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks. arXiv preprint arXiv:2209.07924.
Wang, X., & Shen, H. W. GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries. In The Twelfth International Conference on Learning Representations. Müller, P., Faber, L., Martinkus, K., & Wattenhofer, R. (2024). GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Lu, S., Mills, K. G., He, J., Liu, B., & Niu, D. (2024). GOAt: Explaining graph neural networks via graph output attribution. arXiv preprint arXiv:2401.14578. Wang, X., & Shen, H. W. (2022). Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks. arXiv preprint arXiv:2209.07924.
Generalization in Graph Neural Networks
Supervisors:
Andrés Tello
Date: 2025-02-01
Type: colloquium
Description:
References:
Kanatsoulis, C., & Ribeiro, A. Counting Graph Substructures with Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Liu, H., Feng, J., Kong, L., Liang, N., Tao, D., Chen, Y., & Zhang, M. (2023). One for all: Towards training one graph model for all classification tasks. arXiv preprint arXiv:2310.00149. Lee, H., Yoon, K., 2023. Towards better generalization with flexible representation of multi-module graph neural networks. Transactions on Machine Learning Research. Yu, J., Liang, J., & He, R. (2023). Mind the Label Shift of Augmentation-based Graph OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11620-11630).
Kanatsoulis, C., & Ribeiro, A. Counting Graph Substructures with Graph Neural Networks. In The Twelfth International Conference on Learning Representations. Liu, H., Feng, J., Kong, L., Liang, N., Tao, D., Chen, Y., & Zhang, M. (2023). One for all: Towards training one graph model for all classification tasks. arXiv preprint arXiv:2310.00149. Lee, H., Yoon, K., 2023. Towards better generalization with flexible representation of multi-module graph neural networks. Transactions on Machine Learning Research. Yu, J., Liang, J., & He, R. (2023). Mind the Label Shift of Augmentation-based Graph OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11620-11630).