報告時間：2024年9月29日（星期日）14:30-15:30
報告地點：翡翠湖校區科教樓B座1710室
報 告 人：楊朋昆 副教授
工作單位：清華大學
舉辦單位：數學學院
報告簡介：
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.
報告人簡介：
楊朋昆，清華大學統計與數據科學系副教授，本科畢業于清華大學，碩士博士畢業于伊利諾伊大學香檳分校，普林斯頓大學博士后，主要研究方向為機器學習、高維統計、算法與優化，現主持國家自然科學基金青年項目，入選國家級青年人才，成果發表于AoS, JMLR, TIT, NeurIPS, COLT等期刊與會議上，多次獲得IEEE等國際學會獎項。
