| 2024 | FUSION | Regularization-Based Efficient Continual Learning in Deep State-Space Models. | Yuanhang Zhang, Zhidi Lin, Yiyong Sun, Feng Yin, Carsten Fritsche |
| 2024 | ICASSP | Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models. | Zhidi Lin, Juan Maroas, Ying Li, Feng Yin, Sergios Theodoridis |
| 2024 | ICML | Preventing Model Collapse in Gaussian Process Latent Variable Models. | Ying Li, Zhidi Lin, Feng Yin, Michael Minyi Zhang |
| 2023 | ICASSP | Output-Dependent Gaussian Process State-Space Model. | Zhidi Lin, Lei Cheng, Feng Yin, Lexi Xu, Shuguang Cui |
| 2022 | FUSION | Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data. | Richard Cornelius Suwandi, Zhidi Lin, Yiyong Sun, Zhiguo Wang, Lei Cheng, Feng Yin |
| 2021 | ICASSP | Graph Neural Network for Large-Scale Network Localization. | Wenzhong Yan, Di Jin, Zhidi Lin, Feng Yin |
| 2020 | UAI | An Interpretable and Sample Efficient Deep Kernel for Gaussian Process. | Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis, Shuguang Cui |