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20180418-9 徐易:Accelerated Stochastic Subgradient Methode under Local Error

2018-4-12 21:09| 发布者: 程一-计算所| 查看: 1311| 评论: 0

摘要: 报告嘉宾:徐易(The University of Iowa)报告时间:2018年04月18日(星期三)早上10:30(北京时间)报告题目:Accelerated Stochastic SubgradientMethode under Local Error Bound Condition主持人:钟燕飞(武汉 ...

报告嘉宾:徐易(The University of Iowa)

报告时间:2018年04月18日(星期三)早上10:30(北京时间)

报告题目:Accelerated Stochastic Subgradient Methode under Local Error Bound Condition

主持人:张利军(南京大学


报告人简介:

爱荷华大学计算机系博士在读,本科毕业于浙江大学统计学专业。

讲者个人主页:

https://homepage.cs.uiowa.edu/~yxu71/


相关文献: 

1. Stochastic convex optimization: faster local growth implies faster global convergence, Yi Xu, Qihang Lin, Tianbao Yang, In ICML, 2017.


报告摘要:

In this talk, I will introduce two accelerated stochastic subgradient methods for stochastic non-strongly convex optimization problems by leveraging a generic local error bound condition. The novelty of the proposed methods lies at smartly leveraging the recent historical solution to tackle the variance in the stochastic subgradient. The key idea of both methods is to iteratively solve the original problem approximately in a local region around a recent historical solution with size of the local region gradually decreasing as the solution approaches the optimal set. The difference of the two methods lies at how to construct the local region. The first method uses an explicit ball constraint and the second method uses an implicit regularization approach. For both methods, the improved iteration complexity in a high probability for achieving an ϵ-optimal solution is established. Besides the improved order of iteration complexity with a high probability, the proposed algorithms also enjoy a logarithmic dependence on the distance of the initial solution to the optimal set. 


特别鸣谢本次Webinar主要组织者:

VOOC责任委员:钟燕飞(武汉大学)

VODB协调理事:张利军(南京大学


活动参与方式:

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