Chaofan Li

Isolating Latent Context Information Enhances Graph Structure Learning for Spatial Interpolation
Li, C.; Riedel, T.; Beigl, M.
2025. Advances in Knowledge Discovery and Data Mining. Part II. Ed.: X. Wu, 366 – 377, Springer Nature Singapore. doi:10.1007/978-981-96-8173-0_29 

Feature Deviation Embedding Improves Graph Structure Learning for Spatial Interpolation
Li, C.; Riedel, T.; Beigl, M.
2025. Proceedings of the 2025 SIAM International Conference on Data Mining (SDM), 356 – 365, Society for Industrial and Applied Mathematics Publications (SIAM). doi:10.1137/1.9781611978520.38 

State Graph Based Explanation Approach for Black-Box Time Series Model
Huang, Y.; Li, C.; Lu, H.; Riedel, T.; Beigl, M.
2023. Explainable Artificial Intelligence – First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III, Ed.: L. Longo, 153 – 164, Springer Nature Switzerland. doi:10.1007/978-3-031-44070-0_8  

SmartAQnet 2020: A New Open Urban Air Quality Dataset from Heterogeneous PM Sensors
Li, C.; Budde, M.; Tremper, P.; Schäfer, K.; Riesterer, J.; Redelstein, J.; Petersen, E.; Khedr, M.; Liu, X.; Köpke, M.; Hussain, S.; Ernst, F.; Kowalski, M.; Pesch, M.; Werhahn, J.; Hank, M.; Philipp, A.; Cyrys, J.; Schnelle-Kreis, J.; Grimm, H.; Ziegler, V.; Peters, A.; Emeis, S.; Riedel, T.; Beigl, M.
2022. ProScience, 8. doi:10.14644/dust2021.001  

SmartAQnet 2020 Dataset
Li, C.; Budde, M.
2022. doi:10.35097/540 

Neural Kernel Network Deep Kernel Learning for Predicting Particulate Matter from Heterogeneous Sensors with Uncertainty
Li, C.; Riedel, T.; Beigl, M.
2022. Information Integration and Web Intelligence – 24th International Conference, iiWAS 2022, Virtual Event, November 28–30, 2022, Proceedings. Ed.: E. Pardede, 252–266, Springer Nature Switzerland. doi:10.1007/978-3-031-21047-1_22