报告题目：Learning and Application of Mixed Latent Forest Models

报告时间：2021年12月28日上午9：30

报告地点：南湖校区科研楼616

主讲人：周灿

主讲人简介：东北师范大学数学与统计学院2018级博士研究生，本硕博就读于东北师范大学，主要研究方向为图模型，博士期间在机器学习重点期刊JMLR上发表学术论文，获得东北师范大学2020～2021学年博士研究生国家奖学金、第22届“理想与成才”报告团（研究生）年度人物。

报告摘要：Latent structural learning has attracted more attention in recent years. But most related works only focuses on pure continuous or pure discrete data. In this paper, we consider mixed latent forest models for mixed data mining. We address the latent structural learning and parameter estimation for those mixed models. For structural learning, we propose a consistent bottom-up algorithm, and give a finite sample bound guarantee for the exact structural recovery. For parameter estimation, we suggest a moment estimator by exploiting matrix decomposition, and prove asymptotic normality of the estimator. Experiments on the simulated and real data support that our method is valid for mining the hierarchical structure and latent information.