报告题目：Repro Samples Method for Irregular Inference Problems and for Unraveling Machine Learning Blackboxes
报告摘要：Rapid data science developments require us to have new and revolutionary frameworks to tackle highly non-trivial “irregular inference problems”, e.g., those involving discrete or non-numerical parameters and those involving non-numerical data, etc. This talk presents an innovative, wide-reaching, and effective simulation-inspired framework, called repro samples method, to conduct statistical inference for the irregular problems plus more. We develop both exact and approximate (asymptotic) theories to support the development and provide effective computing algorithms for problems in which explicit solutions are not available. The method is general, likelihood-free, and is particularly effective for irregular inference problems. Particularly, for often-seen irregular inference problems that involve both discrete (or nonnumerical) and continuous parameters, we propose an effective three-step procedure to make inference for all parameters and develop a unique matching scheme that turns the disadvantage of lacking tools to handle discrete/nonnumerical parameters into an advantage of improving computational efficiency. The effectiveness of the method is illustrated by solving two open inference problems in statistics: a) how to construct a confidence set for the unknown number of components in a normal mixture model; b) how to construct confidence sets for the unknown true model, the regression coefficients, or both true model and coefficients jointly in a high dimensional regression model. Comparison studies show that the method has far superior performance to existing attempts. Although the two case studies pertain to the traditional statistics models, the method also has direct extensions to complex machine learning models, e.g., (ensemble) tree models, neural networks, graphical models, etc. It provides a new toolbox to develop interpretable AI and unravel machine learning blackboxes.
报告人简介：谢敏⾰教授是新泽西州立⼤学罗格斯⼤学统计系杰出教授，是美国统计学会会士、国际数理统计学会会士、国际统计学会推选会员，是著名统计学期刊The American Statistician的主编（美国统计学会历史第二悠久的期刊），The New England Journal of Statistics in Data Science创始主编（新英格兰统计学会旗舰期刊），还是JASA、Statistical Science、Science China-Mathematics等多个著名统计学、数学期刊的编委。谢敏革教授是统计学基础理论和融合学习领域的著名专家，在置信分布⽅面的开创性和突破性研究被描述为“充满活⼒和洞察⼒的基础过程”。他的研究兴趣还包括数据科学基础、保形预测、大规模数据分析、估计方程、稳健统计、统计渐近理论等。迄今，他已经在统计学、计算机科学、⽣物医学研究⽅面等方面发表了100余篇⽂章。