嘉宾介绍

陈卫

  陈卫现为微软亚洲研究院高级研究员,清华大学交叉信息学院兼职教授,和中科院计算所客座研究员。他的主要研究方向包括社交和信息网络,在线机器学习,网络博弈论和经济学,分布式计算,容错和可靠系统等。他在社交网络影响力最大化方面的一系列研究在KDD, ICDM, AAAI, VLDB等顶级学术上发表后影响十分广泛,自2009年第一篇KDD论文后他引已超过3500次。他在组合在线学习方面自2013年来在ICML, NIPS上发表了一系列论文,引领了这个方向的研究。陈卫是中国计算机协会第一届大数据专家委员会成员和《大数据》期刊的编委,也是多个顶级学术会议 (KDD, WWW, WSDM, SIGMOD等)的程序委员会成员,他与合作者撰写了一本关于社会影响力传播模型和最大化的专著“Information and Influence Propagation in Social Networks”, Morgan & Claypool 2013. 他的博士论文曾荣获2000 William C. Carter 奖。他与合作者在社交网络社区检测方面的工作获得2010 EMCL PKDD 最佳学生论文奖。

特邀报告

报告题目:组合在线学习(Combinatorial Online Learning)

  报告摘要:Combinatorial optimization is one of the core areas in theoretical computer science and operations research, with many classical problems such as graph shortest paths, minimum spanning trees, maximum weighted matchings, and it also has numerous modern applications such as in networking, online advertising, crowd sourcing, and viral marketing. However, in many of these modern applications, the exact parameters needed as inputs, such as link latencies in wireless networking, click-through rates in online advertising, worker-task performance in crowd sourcing, and diffusion probabilities in viral marketing, are stochastic and unknown, and thus have to be learned over time. On the other hand, well-studied online learning and optimization frameworks, exemplified by the classical multi-armed bandit problem, cannot be applied directly to address these problems due to the exponential blowup in the solution space. Therefore, it demands a new framework that could incorporate combinatorial learning seamlessly into the existing combinatorial optimization framework, without re-engineering the optimization tasks from scratch. In this talk, I will introduce a series of works I and my collaborators have done in the recent years to systematically build such a framework. I will first introduce our work on the general combinatorial multi-armed bandit (CMAB) framework that incorporates optimization tasks with non-linear objective functions and approximation guarantees, and provide a modularized learning algorithm with tight regret analysis. I will then introduce our recent studies including CMAB with probabilistically triggered arms, CMAB with general reward functions, and combinatorial pure exploration, which cover different aspects of combinatorial online learning. Throughout my talk I will illustrate how the framework and the results can be applied to applications such as online advertising, crowd-sourcing, and viral marketing, and discuss many opportunities in further advancing this line of research.