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【数理学院】Learning from Networked Examples

讲座标题:【数理学院】Learning from Networked Examples

主讲人: 王彧弋

讲座时间:2019-09-25 13:00:00

讲座地点:临港校区2教203教室

讲座语言:中文

主办单位:数理学院


讲座内容:

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample because two or more training examples may share some common objects, and hence share the features of these shared objects. We show that the classic approach of ignoring this problem potentially can have a harmful effect on the accuracy of statistics, and then consider alternatives. One of these is to only use independent examples, discarding other information. However, this is clearly suboptimal. We analyze sample error bounds in this networked setting, providing significantly improved results. An important component of our approach is formed by efficient sample weighting schemes, which leads to novel concentration inequalities.


主讲人概况:

王彧弋,X-Order Lab Leader, 苏黎世联邦理工计算机科学博士后。 主要研究方向:理论计算机科学(区块链理论、计算经济学等)和机器学习(偏理论算法研究)。 学术成果:十余篇A+级计算机会议/期刊论文;3篇A级计算机会议/期刊论文。具体学术成果见个人主页:https://disco.ethz.ch/members/yuwang。 现在在主持X-Order Research Lab研究工作。

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