| Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency Z Liu, H Hu, S Zhang, H Guo, S Ke, B Liu, Z Wang arXiv preprint arXiv:2309.17382, 2023 | 10* | 2023 |
| Quantifying the Impact of Label Noise on Federated Learning S Ke, C Huang, X Liu The AAAI 2023 Workshop on Representation Learning for Responsible Human …, 2022 | 6 | 2022 |
| Incentivizing Data Contribution in Cross-Silo Federated Learning C Huang, S Ke, C Kamhoua, P Mohapatra, X Liu arXiv preprint arXiv:2203.03885, 2022 | 5 | 2022 |
| Duopoly business competition in cross-silo federated learning C Huang, S Ke, X Liu IEEE Transactions on Network Science and Engineering, 2023 | 4 | 2023 |
| On the impact of label noise in federated learning S Ke, C Huang, X Liu 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad …, 2023 | 1 | 2023 |
| On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? S Ke, C Hou, G Fanti, S Oh arXiv preprint arXiv:2402.18905, 2024 | | 2024 |
| How Can LLM Guide RL? A Value-Based Approach S Zhang, S Zheng, S Ke, Z Liu, W Jin, J Yuan, Y Yang, H Yang, Z Wang arXiv preprint arXiv:2402.16181, 2024 | | 2024 |
Chao HuangUniversity of California, DavisVerified email at ucdavis.edu
