【专家简介】 练恒，现任香港城市大学数学系教授，于2000年在中国科学技术大学获得数学和计算机学士学位，2007年在美国布朗大学获得计算机硕士，经济学硕士和应用数学博士学位。先后在新加坡南洋理工大学，澳大利亚新南威尔士大学，和香港城市大学工作。在高水平国际期刊上发表学术论文30多篇，包括《Annals of Statistics》《Journal of the Royal Statistical Society，Series B》《Journal of the American Statistical Association》《Journal of Machine Learning Research》《IEEE Transactions on Pattern Analysis and Machine Intelligence》。研究方向包括高维数据分析，函数数据分析，机器学习等。
【报告摘要】Discrimination problems are of significant interest in the machine learning literature. There has been growing interest in extending traditional vector-based machine learning techniques to their matrix forms. In this paper, we investigate the statistical properties of the nuclear-norm-based regularized linear support vector machines, in particular establishing the convergence rate of the estimator in the high-dimensional setting. Furthermore, within the distributed estimation paradigm, we propose a communication-efficient estimator that can achieve the same convergence rate. We illustrate the performances of the estimators via some simulation examples and an empirical data analysis.
腾讯会议号：512 467 288
报告时间：9月5日（周一）下午14:00-15:00
