Quantifying the Impact of Label Noise on Federated Learning. - Shuqi Ke; Chao Huang 028; Xin Liu 002 (CoRR, 2022)

On the Impact of Label Noise in Federated Learning. - Shuqi Ke; Chao Huang 028; Xin Liu 002 (WiOpt, 2023)

Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency. - Zhihan Liu; Hao Hu; Shenao Zhang; Hongyi Guo; Shuqi Ke; Boyi Liu 001; Zhaoran Wang 001 (CoRR, 2023)

Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents. - Zhihan Liu; Hao Hu; Shenao Zhang; Hongyi Guo; Shuqi Ke; Boyi Liu 001; Zhaoran Wang 001 (ICML, 2024)

How Can LLM Guide RL? A Value-Based Approach. - Shenao Zhang; Sirui Zheng; Shuqi Ke; Zhihan Liu; Wanxin Jin; Jianbo Yuan; Yingxiang Yang; Hongxia Yang; Zhaoran Wang 001 (CoRR, 2024)

On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune? - Shuqi Ke; Charlie Hou; Giulia Fanti; Sewoong Oh (CoRR, 2024)

Duopoly Business Competition in Cross-Silo Federated Learning. - Chao Huang 028; Shuqi Ke; Xin Liu 002 (IEEE Trans. Netw. Sci. Eng., 2024)