报告人：邵明杰
单位： 山东大学信息科学与工程学院
报告地点：工程实训中心1601
报告时间：12月6日下午2:00
报告题目：
Accelerated and Deep Expectation-Maximization Method for Quantized Linear Regression
摘要：
In this talk, we delve into the realm of parameter estimation from quantized data, with a particular focus on quantized linear regression (QLR). QLR finds its applications in various domains, including signal processing, data analysis, and wireless communication. Our primary objective is to explore the maximum-likelihood (ML) estimation for QLR and its solving algorithm: the expectation maximization (EM) algorithm. To begin, we investigate the convergence rate of the EM algorithm for the QLR problem. By establishing a link between EM and the proximal gradient method, we gain valuable insights into the convergence analysis. Notably, we uncover how system parameters influence the rate at which EM converges. This understanding paves the way for developing novel accelerated and/or inexact EM schemes. We present convergence rate results to validate the efficacy of these new schemes. Furthermore, we introduce a deep EM algorithm. This novel algorithm leverages an efficient structured deep neural network that is based on the principles of EM. By integrating deep learning techniques into the EM framework, we aim to enhance the algorithm's performance and computational efficiency. Simulation results unequivocally demonstrate that our algorithms outperform the standard EM counterpart in terms of speed and efficiency.

报告人简介：
邵明杰，研究员，齐鲁青年学者，山东大学信息科学与工程学院。2015年于西安电子科技大学通信工程专业“卓越工程师教育培养计划”获得学士学位，2020年于香港中文大学电子工程系获得博士学位，入选“香港政府博士奖学金（HKPFS）”计划。2020年至2023年期间于香港中文大学电子工程系从事博士后研究工作。2022年访问中国科学院数学与系统科学研究院。2023年入职山东大学信息科学与工程学院，聘为“齐鲁青年学者”。 IEEE会员。担任IEEE TSP 、IEEE TWC、IEEE TVT、IEEE TCOM、IEEE JSTSP等多个国际期刊的审稿人。
主要研究方向包括：1）信号处理和机器学习在无线通信中的研究；2）统计信号处理与机器学习；3）优化理论与算法。最新研究方向主要包含低精度量化信号处理、优化算法与深度学习的交叉应用。近年来在IEEE TSP、IEEE JSTSP、IEEE ICASSP等顶级期刊与会议发表SCI/EI论文三十余篇，多篇文章入选IEEE期刊最受欢迎前五十文章列表。