Learning from networked examples
主讲人：王彧弋 博士
时间：1月14日10：00~11：00
地点：理工楼401
摘要：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.
个人简介：王彧弋 博士，瑞士苏黎世联邦理工学院博士后研究员。他于2015年从比利时鲁汶大学获得博士学位，2011年从新加坡国立大学获得硕士学位，2009年本科毕业于华中科技大学。他现在的研究兴趣是机器学习与理论计算机科学。