3 papers
cs.LG2026
Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
Xiang Chen, Yuling Shi, Qizhen Lan +4
LLM agents are widely deployed in complex interactive tasks, yet privacy constraints often preclude centralized optimization and co-evolution across dynamic environments. Despite t…
cs.LG2025
Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
Xuesong Jia, Yuanjie Shi, Ziquan Liu +2
Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid…
cs.LG2025
Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification
Zijian Zhang, Xinyu Chen, Yuanjie Shi +3
Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential fo…