报告人： 周峰 副教授

报告题目： Score Matching for Estimating Finite Point Processes

时间： 2026年11月16日（周一）09:00—12:00

地点： 西安交通大学彭康楼122会议室

邀请人： 束俊  教授

报告摘要：

Score matching estimators have gained widespread attention in recent years partly because they are free from calculating the integral of normalizing constant, thereby addressing the computational challenges in maximum likelihood estimation (MLE). Some existing works have proposed score matching estimators for point processes. However, this work demonstrates that the incompleteness of the estimators proposed in those works renders them applicable only to specific problems, and they fail for more general point processes. To address this issue, this work introduces the weighted score matching estimator to point processes. Theoretically, we prove the consistency of our estimator and establish its rate of convergence. Experimental results indicate that our estimator accurately estimates model parameters on synthetic data and yields results consistent with MLE on real data. In contrast, existing score matching estimators fail to perform effectively.

报告人简介：

周峰，中国人民大学统计学院副教授，主要研究领域包括统计机器学习、贝叶斯方法、高效大模型等，主持国家自然科学基金青年项目、面上项目，参与新一代人工智能国家科技重大专项课题，在JMLR、MLJ、STCO、ICML、NeurIPS、ICLR、AAAI、KDD等国际期刊和会议上已发表论文40余篇，担任ICML、NeurIPS、ICLR、AISTATS等国际会议领域主席，国际期刊《Statistics and Computing》副主编，《Transactions on Machine Learning Research》执行编辑，《Journal of Machine Learning Research》编委。