报告题目:Repro Samples Method for Irregular Inference Problems and for Unraveling Machine Learning Blackboxes报告人:谢敏革教授报告摘要:Rapid data science developments require us to have new and revolutionary frameworks to tackle highl...[详细]
报告题目:Word embeddings via causal inference: Gender bias reducing and semantic information preserving 报告人:孔令龙教授 报告摘要:With widening deployments of natural language processing (NLP) in daily life,inherited social biases fr...[详细]
报告题目: Causal Inference on Distribution Functions 报告人:孔德含教授 报告摘要:Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclus...[详细]
报告题目:Non-nested model selection based on empirical likelihood ratio tests.报告人:蒋建成教授报告摘要: We propose an empirical likelihood ratio (ELR) test for comparing any two supervised learning models, which may be nested, non-n...[详细]
报告人:宋健教授报告时间:5月27日(周 六)下午15:00—16:00报告地点:6165cc金沙总站检测中心211报告题目:Moments and asymptotics for a class of SPDEs with space-time white noise报告摘要:For a class of stochastic partial differential equations...[详细]
报告题目:Checking the Adequacy of Quantile Regression Models报告人:宋晓军 副教授 (北京大学)报告时间:2023年5月31日上午 11:00-12:00报告地点:学院会议室213 报告摘要:We propose a new class of tests to evaluate the correct specification ...[详细]
报告题目:Robust Inference for Misspecified Threshold Regression and Regression Tree Analysis报告人:于萍 副教授 (香港大学)报告时间:2023年5月31日上午 10:00-11:00报告地点:学院会议室213 报告摘要:In this paper, we develop the asymptotic...[详细]
(通讯员:朱雪瑜) 为了学习贯彻习近平新时代中国特色社会主义思想主题教育,贯彻落实教育部关于实施“研究生教育创新计划”精神,认真贯彻百花齐放、百家争鸣和学术自由的方针,鼓励和支持广大研究生追求真理、学术争鸣、勇于探索、献身科学的精神,为...[详细]