By Dr Yu Guang Wang

Conference Papers

Huang K; Wang YG; Li M; Liò P, 2024, 'How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing', in Proceedings of Machine Learning Research, PMLR, Vienna, Austria, pp. 20310 - 20330, presented at 41st International Conference on Machine Learning, Vienna, Austria, 21 July 2024, https://proceedings.mlr.press/v235/huang24z.html

Yu X; Yi K; Wang YG; Shen Y, 2024, 'A Regressor-Guided Graph Diffusion Model for Predicting Enzyme Mutations to Enhance Turnover Number', in Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, pp. 3943 - 3948, http://dx.doi.org/10.1109/BIBM62325.2024.10822301

Wu T; Wang YG; Shen Y, 2024, 'LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble', in Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, pp. 3850 - 3855, http://dx.doi.org/10.1109/BIBM62325.2024.10821758

Liu Y; Chen Z; Wang YG; Shen Y, 2024, 'TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering', in Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, pp. 5395 - 5400, http://dx.doi.org/10.1109/BIBM62325.2024.10822318

Chen Z; Liu Y; Wang YG; Shen Y, 2024, 'Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab', in Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024, pp. 5364 - 5370, http://dx.doi.org/10.1109/BIBM62325.2024.10822562

Li M; Sonoda S; Cao F; Wang YG; Liang J, 2023, 'How Powerful are Shallow Neural Networks with Bandlimited Random Weights?', in Proceedings of Machine Learning Research, Honolulu, Hawaii, pp. 19360 - 19384, presented at 40th International Conference on Machine Learning (ICML 2023), Honolulu, Hawaii, 23 July 2023, https://proceedings.mlr.press/v202/li23aa.html

Zhou B; Jiang Y; Wang Y; Liang J; Gao J; Pan S; Zhang X, 2023, 'Robust Graph Representation Learning for Local Corruption Recovery', in ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, pp. 438 - 448, http://dx.doi.org/10.1145/3543507.3583399

Wang Y; Yi K; Liu X; Wang YG; Jin S, 2023, 'ACMP: ALLEN-CAHN MESSAGE PASSING WITH ATTRACTIVE AND REPULSIVE FORCES FOR GRAPH NEURAL NETWORKS', in 11th International Conference on Learning Representations, ICLR 2023

Ke X; Zhu H; Yi K; He G; Yang G; Wang YG, 2023, 'Adaptive Importance Sampling and Quasi-Monte Carlo Methods for 6G URLLC Systems', in IEEE International Conference on Communications, pp. 5272 - 5278, http://dx.doi.org/10.1109/ICC45041.2023.10279562

Xu C; Tan RT; Tan Y; Chen S; Wang YG; Wang X; Wang Y, 2023, 'EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1410 - 1420, http://dx.doi.org/10.1109/CVPR52729.2023.00142

Yi K; Zhou B; Shen Y; Liò P; Wang YG, 2023, 'Graph Denoising Diffusion for Inverse Protein Folding', in Advances in Neural Information Processing Systems

Shen Y; Zhou B; Xiong X; Gao R; Wang YG, 2023, 'How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images', in Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, pp. 2227 - 2230, http://dx.doi.org/10.1109/BIBM58861.2023.10385379

Banerjee PK; Karhadkar K; Wang YG; Alon U; Montufar G, 2022, 'Oversquashing in GNNs through the lens of information contraction and graph expansion', in 2022 58th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2022, http://dx.doi.org/10.1109/Allerton49937.2022.9929363

Zhou B; Liu X; Liu Y; Huang Y; Liò P; Wang YG, 2022, 'Well-conditioned Spectral Transforms for Dynamic Graph Representation', in Proceedings of Machine Learning Research

Zheng X; Zhou B; Gao J; Wang YG; Liò P; Li M; Montúfar G, 2021, 'How Framelets Enhance Graph Neural Networks', in Proceedings of Machine Learning Research, pp. 12761 - 12771

Bodnar C; Frasca F; Otter N; Wang YG; Liò P; Montúfar G; Bronstein M, 2021, 'Weisfeiler and Lehman Go Cellular: CW Networks', in Advances in Neural Information Processing Systems, pp. 2625 - 2640

Bodnar C; Frasca F; Wang YG; Otter N; Montúfar G; Liò P; Bronstein MM, 2021, 'Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks', in Proceedings of Machine Learning Research, pp. 1026 - 1037

Wang YG; Zhuang X, 2019, 'Tight framelets on graphs for multiscale data analysis', in Proceedings of SPIE - The International Society for Optical Engineering, http://dx.doi.org/10.1117/12.2528414

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