FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models. - Panlong Wu; Kangshuo Li; Ting Wang; Fangxin Wang 001 (CoRR, 2023)

Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty. - Changbin Li; Kangshuo Li; Yuzhe Ou; Lance M. Kaplan; Audun Jøsang; Jin-Hee Cho; Dong Hyun Jeong; Feng Chen 001 (ICLR, 2024)

Uncertainty Quantification for Bird's Eye View Semantic Segmentation: Methods and Benchmarks. - Linlin Yu; Bowen Yang; Tianhao Wang; Kangshuo Li; Feng Chen 001 (CoRR, 2024)

Federated In-Context LLM Agent Learning. - Panlong Wu; Kangshuo Li; Junbao Nan; Fangxin Wang 001 (CoRR, 2024)

FedFMSL: Federated Learning of Foundation Models With Sparsely Activated LoRA. - Panlong Wu; Kangshuo Li; Ting Wang; Yanjie Dong; Victor C. M. Leung; Fangxin Wang 001 (IEEE Trans. Mob. Comput., 2024)

Evidential Uncertainty Probes for Graph Neural Networks. - Linlin Yu; Kangshuo Li; Pritom Kumar Saha; Yifei Lou; Feng Chen 001 (AISTATS, 2025)

Large Language Models with Reinforcement Learning from Human Feedback Approach for Enhancing Explainable Sexism Detection. - Ali Riahi Samani; Tianhao Wang; Kangshuo Li; Feng Chen 001 (COLING, 2025)

Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function. - Linlin Yu; Bowen Yang; Tianhao Wang; Kangshuo Li; Feng Chen 001 (ICLR, 2025)