By Dr Yu Guang Wang

Journal articles

Wang YG; Sloan IH; Womersley RS, 2016, 'Riemann Localisation on the Sphere', Journal of Fourier Analysis and Applications, 24, pp. 1 - 43, http://dx.doi.org/10.1007/s00041-016-9496-4

Wang Y, 2016, 'Filtered polynomial approximation on the sphere', Bulletin of the Australian Mathematical Society, 93, pp. 162 - 163, http://dx.doi.org/10.1017/S000497271500132X

Cao F; Wang D; Zhu H; Wang Y, 2016, 'An iterative learning algorithm for feedforward neural networks with random weights', Information Sciences, 328, pp. 546 - 557, http://dx.doi.org/10.1016/j.ins.2015.09.002

Brauchart JS; Reznikov AB; Saff EB; Sloan IH; Wang YG; Womersley RS, 2015, 'Random Point Sets on the Sphere --- Hole Radii, Covering, and Separation',

Brauchart JS; Dick J; Saff EB; Sloan IH; Wang YG; Womersley RS, 2015, 'Covering of spheres by spherical caps and worst-case error for equal weight cubature in Sobolev spaces', Journal of Mathematical Analysis and Applications, 431, pp. 782 - 811, http://dx.doi.org/10.1016/j.jmaa.2015.05.079

Wang Y; Cao F, 2014, 'Approximation by semigroup of spherical operators', Frontiers of Mathematics in China, 9, pp. 387 - 416, http://dx.doi.org/10.1007/s11464-014-0361-y

Chen ZX; Zhu HY; Wang YG, 2013, 'A modified extreme learning machine with sigmoidal activation functions', Neural Computing and Applications, 22, pp. 541 - 550, http://dx.doi.org/10.1007/s00521-012-0860-2

Wang Y; Cao F, 2011, 'Approximation by Boolean sums of Jackson operators on the sphere', Journal of Computational Analysis and Applications, 13, pp. 830 - 842

Wang Y; Cao F; Yuan Y, 2011, 'A study on effectiveness of extreme learning machine', Neurocomputing, 74, pp. 2483 - 2490, http://dx.doi.org/10.1016/j.neucom.2010.11.030

Yuan Y; Wang Y; Cao F, 2011, 'Optimization approximation solution for regression problem based on extreme learning machine', Neurocomputing, 74, pp. 2475 - 2482, http://dx.doi.org/10.1016/j.neucom.2010.12.037

Cao F; Wang Y, 2009, 'The direct and converse inequalities for jackson-type operators on spherical cap', Journal of Inequalities and Applications, 2009, pp. 205298, http://dx.doi.org/10.1155/2009/205298

Conference Papers

Liu Y; Chen Z; Wang YG; Shen Y, 2025, 'AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering', in Proceedings International Conference on Computational Linguistics Coling, pp. 422 - 430

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

Working Papers

Yi K; Chen J; Zhou B; Lio P; Fan Y; Hamann J, 2022, Approximate Equivariance SO(3) Needlet Convolution, http://dx.doi.org, https://arxiv.org/abs/2206.10385

Wang YG; Li M; Ma Z; Montufar G; Zhuang X; Fan Y, 2020, Haar graph pooling, http://dx.doi.org

Hamann J; Gia QTL; Sloan IH; Wang YG; Womersley RS, 2019, A New Probe of Gaussianity and Isotropy applied to the CMB Maps, http://dx.doi.org, http://arxiv.org/abs/1911.11442v2

Preprints

Ma Y; Yi K; Lio P; Jin S; Wang YG, 2025, How Particle System Theory Enhances Hypergraph Message Passing, http://arxiv.org/abs/2505.18505v1

Zhou Y; Ge Y; Dong X; Wang Y, 2024, How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliability, http://arxiv.org/abs/2406.12915v5

Shen Y; Chen Z; Mamalakis M; He L; Xia H; Li T; Su Y; He J; Wang YG, 2024, A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding, http://arxiv.org/abs/2406.05540v2

Yi K; Zhou B; Shen Y; Liò P; Wang YG, 2023, Graph Denoising Diffusion for Inverse Protein Folding, http://arxiv.org/abs/2306.16819v2

Liu X; Zhou B; Zhang C; Wang YG, 2023, Framelet Message Passing, http://arxiv.org/abs/2302.14806v1

Shen Y; Zhou B; Xiong X; Gao R; Wang YG, 2022, How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images, http://arxiv.org/abs/2206.07599v1

Wang Y; Yi K; Liu X; Wang YG; Jin S, 2022, ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition, http://arxiv.org/abs/2206.05437v3

Chen H; Wang YG; Xiong H, 2022, Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks, http://arxiv.org/abs/2206.00228v1

Zhou B; Jiang Y; Wang YG; Liang J; Gao J; Pan S; Zhang X, 2022, Robust Graph Representation Learning for Local Corruption Recovery, http://arxiv.org/abs/2202.04936v4

Zhou B; Liu X; Liu Y; Huang Y; Liò P; Wang Y, 2021, Spectral Transform Forms Scalable Transformer, http://arxiv.org/abs/2111.07602v1

Zhou B; Li R; Zheng X; Wang YG; Gao J, 2021, Graph Denoising with Framelet Regularizer, http://arxiv.org/abs/2111.03264v1

Anh VV; Broadbridge P; Olenko A; Wang YG, 2017, On Approximation for Fractional Stochastic Partial Differential Equations on the Sphere, http://dx.doi.org/10.1007/s00477-018-1517-1

Back to profile page