| Towards effective context for meta-reinforcement learning: an approach based on contrastive learning H Fu, H Tang, J Hao, C Chen, X Feng, D Li, W Liu Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7457-7465, 2021 | 43 | 2021 |
| Critical connectivity and fastest convergence rates of distributed consensus with switching topologies and additive noises G Chen, C Chen, G Yin IEEE Transactions on Automatic Control 62 (12), 6152-6167, 2017 | 39 | 2017 |
| Creativity of ai: Automatic symbolic option discovery for facilitating deep reinforcement learning M Jin, Z Ma, K Jin, HH Zhuo, C Chen, C Yu Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 7042-7050, 2022 | 31 | 2022 |
| Fast encoding of polar codes with Reed–Solomon kernel P Trifonov, V Miloslavskaya, C Chen, Y Wang IEEE Transactions on Communications 64 (7), 2746-2753, 2016 | 27 | 2016 |
| CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation X Feng*, C Chen*, D Li, M Zhao, J Hao, J Wang Proceedings of the 30th ACM International Conference on Information …, 2021 | 21 | 2021 |
| What about inputting policy in value function: Policy representation and policy-extended value function approximator H Tang, Z Meng, J Hao, C Chen, D Graves, D Li, C Yu, H Mao, W Liu, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8441-8449, 2022 | 17 | 2022 |
| Stability of diffusion adaptive filters C Chen, Z Liu, L Guo IFAC Proceedings Volumes 47 (3), 10409-10414, 2014 | 13 | 2014 |
| Performance bounds of distributed adaptive filters with cooperative correlated signals C Chen, Z Liu, L Guo Science China. Information Sciences 59 (11), 112202, 2016 | 11 | 2016 |
| Learning Pseudometric-based Action Representations for Offline Reinforcement Learning P Gu, M Zhao, C Chen, D Li, J Hao, B An Proceedings of the 39th International Conference on Machine Learning, 2021 | 10 | 2021 |
| Towards comprehensive maneuver decisions for lane change using reinforcement learning C Chen, J Qian, H Yao, J Luo, H Zhang, W Liu NeurIPS Workshop on Machine Learning for Intelligent Transportation Systems 2018, 2018 | 10 | 2018 |
| Addressing action oscillations through learning policy inertia C Chen, H Tang, J Hao, W Liu, Z Meng Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 7020-7027, 2021 | 9 | 2021 |
| Plan to predict: Learning an uncertainty-foreseeing model for model-based reinforcement learning Z Wu, C Yu, C Chen, J Hao, HH Zhuo Advances in Neural Information Processing Systems 35, 15849-15861, 2022 | 8 | 2022 |
| Consensus of flocks under M-nearest-neighbor rules C Chen, G Chen, L Guo Journal of Systems Science and Complexity 28 (1), 1-15, 2015 | 8 | 2015 |
| On the minimum number of neighbors needed for consensus of flocks C Chen, G Chen, L Guo Control Theory and Technology 15 (4), 327-339, 2017 | 7 | 2017 |
| MGHRL: Meta goal-generation for hierarchical reinforcement learning H Fu, H Tang, J Hao, W Liu, C Chen Distributed Artificial Intelligence: Second International Conference, DAI …, 2020 | 6 | 2020 |
| Performance analysis of distributed adaptive filters C Chen, Z Liu, L Guo Communications in Information and Systems 15 (4), 453-476, 2015 | 6 | 2015 |
| Synchronization of mobile autonomous agents with M-nearest-neighbor rule C Chen, G Chen, L Guo Proceedings of the 31st Chinese Control Conference, 1147-1152, 2012 | 6 | 2012 |
| Rethinking Reinforcement Learning based Logic Synthesis C Wang*, C Chen*, D Li, B Wang arXiv preprint arXiv:2205.07614, 2022 | 4 | 2022 |
| Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning J Shao, Y Qu, C Chen, H Zhang, X Ji Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Models as agents: optimizing multi-step predictions of interactive local models in model-based multi-agent reinforcement learning Z Wu, C Yu, C Chen, J Hao, HH Zhuo Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10435 …, 2023 | 2 | 2023 |
