• 美国北卡罗来纳大学夏洛特分校蒋建成教授学术报告 2024-12-11

    报告题目:Partition-Insensitive Parallel ADMM Algorithm for High-dimensional Linear Models报告人:蒋建成报告时间:2024年12月17日 上午 8点30分—9点30分报告地点:腾讯会议(756 430 668)报告摘要:Abstract. The parallel alternating direction...[详细]

  • 学术报告——武汉大学胡亦钧教授 2024-12-03

    报告题目:On optimal reinsurance in the presence of premium budget constraint and reinsurer's risk limit报告摘要:In this talk, I will present two new optimal reinsurance models in which both premium budget constraints and the reinsurer'...[详细]

  • 统计机器学习与因果效应评估系列学术报告(14)——英国爱丁堡大学陈泽汛博士报告 2024-12-03

    报告题目:Economics-Aware Gaussian Process Modelling for Option-Implied Risk Metrics报告人: 陈泽汛 博士 (英国爱丁堡大学)报告时间:2024年12月05日下午 19:00-20:00报告地点:腾讯会议(693-776-383)报告摘要:Machine learning models are pre...[详细]

  • 山东大学嵇少林教授学术报告 2024-11-19

    报告题目:   A novel control method for solving high-dimensional Hamiltonian systems through deep neural networks 报告摘要:  A novel control method is proposed to solve a high-dimensional stochastic Hamiltonian system with boundary cond...[详细]

  • 加拿大Concordia大学周晓文教授学术报告 2024-11-19

    报告题目:Optimal Dividend Problem for a Risk Process with Endogenous Regime Switching报告时间:2024.11.20  21:00-22:00报告地点:腾讯会议782-957-226报告人简介:周晓文教授,1988年本科毕业于中山大学,1999年在加州大学Berkeley 分校 获得统...[详细]

  • 山东财经大学统计与数学学院戴洪帅教授学术报告 2024-11-19

    报告题目:Optimal Staffing Strategies for Dynamic Queueing Systems with Two- Demand Patterns报告时间:2024.11.20 16:30-17:30报告地点:统计与数据科学学院 106报告人简介:戴洪帅,山东财经大学统计与数学学院教授。2004年本科毕业于曲阜师范大...[详细]

  • 【11月26日】学术报告:南开大学王兆军教授 2024-11-18

    报告题目:Semi-supervised distribution learning摘   要:This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work ex...[详细]

  • 【11月26日】学术报告:中国科学院何煦副研究员 2024-11-18

    报告题目:A distance metric-based uniform subsampling method for nonparametric models摘  要:Taking subset samples from the original data set is an efficient and popular strategy to handle massive data that is too large to be directly mo...[详细]

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