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郑泽敏教授学术报告

  发布日期:2017-06-15  浏览量:478


报告题目: Scalable Interpretable Multi-Response Regression via SEED

: 郑泽敏(中国科学技术大学管理学院统计与金融系特任正教授)

报告时间: 2017615(周四)  16:30-17:30

报告地点: 磬苑校区澳门赌搏网站大全H306

报告摘要:Sparse reduced-rank regression is an important tool to uncover meaningful dependence structure between large numbers of predictors and responses in many big data applications such as genome-wide association studies and social media analysis. Despite the recent theoretical and algorithmic advances, scalable estimation of sparse reduced-rank regression remains largely unexplored. In this paper, we suggest a scalable procedure called sequential estimation with eigen-decomposition (SEED) which needs only a single top-r sparse singular value decomposition from a generalized eigenvalue problem to find the optimal low-rank and sparse matrix. Our suggested method is not only scalable but also performs simultaneous dimensionality reduction and variable selection. Under some mild regularity conditions, we show that SEED enjoys nice sampling properties including consistency in estimation, rank selection, prediction, and model selection. Moreover, SEED employs only basic matrix operations that can be efficiently parallelized in high performance computing devices. Numerical studies on synthetic and real data sets show that SEED outperforms the state-of-the-art approaches for large-scale matrix estimation problem.

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专家概况: 郑泽敏博士毕业于南加州大学,现为中国科学技术大学管理学院统计与金融系特任正教授。他的研究领域是高维统计推断,研究兴趣包括高维情况下的模型选择、分类、传播网络推断以及大数据问题,其研究成果发表在Journal of the Royal Statistical Society Series BThe Annals of StatisticsJournal of Machine Learning Research 等国际统计学顶级刊物上。曾获美国数理统计协会颁发的科研新人奖(IMS Travel Award)以及南加州大学的杰出科研奖(Prize for Excellent in Research)。

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