报告时间：2024年9月29日（星期日）14:30-15:30

报告地点：翡翠科教楼B座1710

报告人：杨朋昆 副教授

工作单位：清华大学

主办单位：数学学院

报告简介：In this talk I will present some recent results for estimating Gaussian location mixtures with known or unknown variance. To overcome the aforementioned theoretic and algorithmic hurdles, a crucial step is to denoise the moment estimates by projecting to the truncated moment space before executing the method of moments. Not only does this regularization ensures existence and uniqueness of solutions, it also yields fast solvers by means of Gaussian quadrature. Furthermore, by proving new moment comparison theorems in Wasserstein distance via polynomial interpolation and marjorization, we establish the statistical guarantees and optimality of the proposed procedure. These results can also be viewed as provable algorithms for Generalized Method of Moments which involves non-convex optimization and lacks theoretical guarantees.

报告人简介：杨朋昆，清华大学统计与数据科学系副教授，本科毕业于清华大学，硕士博士毕业于伊利诺伊大学香槟分校，普林斯顿大学博士后，主要研究方向为机器学习、高维统计、算法与优化，现主持国家自然科学基金青年项目，入选国家级青年人才，成果发表于AoS, JMLR, TIT, NeurIPS, COLT等期刊与会议上，多次获得IEEE等国际学会奖项。