中国科学院数学与系统科学研究院

科研进展与学术交流报告会

（第94期）

报告人：孔令臣 教授（北京交通大学）

题  目：A Novel Conformal Prediction Framework for Differential Privacy

摘  要：As privacy and reliability become increasingly critical in modern statistical machine learning, we introduce a novel differential private conformal prediction (DPCP). It is a framework for constructing model-free private prediction sets to safely enable uncertainty quantification. Compared to existing methods, it has lower computational cost and more efficient data utilization, while maintaining rigorous theoretical guarantees on both privacy and coverage. Moreover, it produces more precise and less conservative sets of predictions. We further analyze the efficiency of DPCP within the frameworks of empirical risk minimization, demonstrating their robustness and adaptability. Numerical experiments on real-world datasets validate the practical effectiveness of our approach.

时  间：2025.1.3(星期五), 10:40-11:50

地  点：南楼204会议室，腾讯会议地址：907-5908-5643