报告题目：Deep Representative Learning

报 告 人：焦雨领

报告时间：2020年9月29日 15:00-16:00

报告地点：腾讯会议 会议ID：749 485 513

报告摘要：

It is believe that success of deep learning depends on its automatic data representation abilities. But few theoretical works to explore this. In this talk, we present a statistical framework to achieve a good data representation that enjoys information preservation, low dimensionality and disentanglement. At the population level, we formulate the ideal representation learning task as finding a nonlinear repesentaion map that minimizes the sum of losses characterizing conditional independence and disentanglement. We estimate the target map at the sample level nonparametrically with deep neural networks. We derive a bound on the excess risk of the deep nonparametric estimator. The proposed method is validated via comprehensive numerical experiments and real data analysis in the context of regression and classification.

报告人简介：

焦雨领，2014 年毕业于武汉大学数学与统计学院。主要从事统计学习、反问题等方面研究。主持国家自然科学基金面上项目、青年项目、湖北省自然科学基金面上项目、统计与数据科学前沿理论及应用教育部重点实验室课题。在包括SIAM Journal on Numerical Analysis, SIAM Journal on Scientific Computing, Applied and Computational Harmonic Analysis, Statistical Science, Journal of Machine Learning Research, ICML, Inverse Problems, IEEE Transactions on Signal Processing， Statistica Sinica，中国科学等在内的期刊和会议上发表 40 余篇论文。

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