| Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning C Qu, S Mannor, H Xu, Y Qi, L Song, J Xiong Neurips 2019, 2019 | 62 | 2019 |
| A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud S Xue, C Qu, X Shi, C Liao, S Zhu, X Tan, L Ma, S Wang, S Wang, Y Hu, ... KDD 2022, 2022 | 42 | 2022 |
| Non-convex Conditional Gradient Sliding C Qu, Y Li, H Xu Proceedings of The 35th International Conference on Machine Learning (ICML-2018), 2017 | 32 | 2017 |
| Subspace Clustering with Irrelevant Features via Robust Dantzig Selector C Qu, H Xu Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015 | 24 | 2015 |
| Nonlinear Distributional Gradient Temporal-Difference Learning C Qu, S Mannor, H Xu ICML2019, 2018 | 18 | 2018 |
| Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness X Tan, S Shi, X Qiu, C Qu, Z Qi, Y Xu, Y Qi EMNLP (industry Track), 2023 | 17 | 2023 |
| Intention Propagation for Multi-agent Reinforcement Learning C Qu, H Li, C Liu, J Xiong, J Zhang, W Chu, Y Qi, L Song arXiv preprint arXiv:2004.08883, 2020 | 16 | 2020 |
| Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes C Qu, X Tan, S Xue, X Shi, J Zhang, H Mei AAAI 2023, 2022 | 15 | 2022 |
| Provably Invariance Learning without Domain Information X Tan, LIN Yong, S Zhu, C Qu, X Qiu, X Yinghui, P Cui, Y Qi ICML2023, 2023 | 13 | 2023 |
| linear convergence of svrg in statistical estimation C Qu, Y Li, H Xu arXiv:1611.01957, 2016 | 12 | 2016 |
| PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching Z Qi, X Tan, S Shi, C Qu, Y Xu, Y Qi EMNLP (industry Track), 2023 | 10 | 2023 |
| The role of orientation diversity in binocular vergence control C Qu, B Shi IJCNN 2011, 2011 | 7 | 2011 |
| Gram-based Attentive Neural Ordinary Differential Equations Network for Video Nystagmography Classification X Qiu, S Shi, X Tan, C Qu, Z Fang, H Wang, Y Gao, P Wu, H Li ICCV, 2023 | 6 | 2023 |
| Fast Rate Analysis of Some Stochastic Optimization Algorithms C Qu, H Xu, CJ Ong Proceedings of The 33rd International Conference on Machine Learning (ICML-2016), 2016 | 5 | 2016 |
| SAGA and Restricted Strong Convexity C Qu, Y Li, H Xu arXiv:1701.07808, 2017 | 4 | 2017 |
| Linear Convergence of SDCA in Statistical Estimation C Qu, H Xu arXiv:1701.07808, 2017 | 4 | 2017 |
| Subequivariant Reinforcement Learning Framework for Coordinated Motion Control H Wang, X Tan, X Qiu, C Qu ICRA2024, 2024 | 2 | 2024 |
| Hybrid Directional Graph Neural Network for Molecules J An, C QU, Z Zhou, F Cao, Y Xu, Y Qi, F Shen ICLR 2024 (spotlight), 2024 | 2* | 2024 |
| Communication-Efficient Projection-Free Algorithm for Distributed Optimization Y Li, C Qu, H Xu https://arxiv.org/abs/1805.07841, 2018 | 1 | 2018 |
| Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Y Li, H Huang, J Kuang, Y Li, SY Guo, C Qu, X Tan, HT Zheng, Y Shen, ... ICLR2025, 2025 | | 2025 |
