Abstract:

Traditionally machine learning and optimization are two different branches in computer science. They need to accomplish two different types of tasks, and they are studied by two different sets of domain experts. Machine learning is the task of extracting a model from the data, while optimization is to find the optimal solutions from the learned model. In the current era of big data and AI, however, such separation may hurt the end-to-end performance from data to optimization in unexpected ways. In this talk, I will introduce the paradigm of data-driven optimization that tightly integrates data sampling, machine learning, and optimization tasks. I will mainly explain two approaches in this paradigm, one is optimization from structured samples, which carefully utilizes the structural information from the sample data to adjust the learning and optimization algorithms; the other is combinatorial online learning, which adds feedback loop from the optimization result to data sampling and learning to improve the sample efficiency and optimization efficacy. I will illustrate these two approaches through my recent research studies in these areas.

Bio:

Wei Chen is a Principal Researcher at Microsoft Research Asia (MSRA) and the Chair of MSRA Theory Center. He has served as an Adjunct Professor or Researcher at several universities and research institutes such as Tsinghua University, Shanghai Jiao Tong University and Chinese Academy of Sciences. He is a standing committee member of the Technical Committee on Theoretical Computer Science, Chinese Computer Federation (CCF), and a member of the CCF Technical Committee on Big Data. He is a Fellow of Institute of Electrical and Electronic Engineers (IEEE).  He is selected as one of the world’s top 2% scientists by a Stanford University ranking in 2020.

Wei Chen’s main research interests include online learning and optimization, social and information networks, network game theory and economics, distributed computing, and fault tolerance. He has done influential research on the algorithmic study of social influence propagation and maximization and combinatorial online learning, with 10000+ collective citations on these topics. He has one coauthored monograph in English in 2013 and one sole authored monograph in Chinese in 2020, both on information and influence propagation in social networks. He has served as editors, academic conference chairs and program committee members for many academic conferences and journals. Wei Chen has Bachelor and Master degrees from Tsinghua University and a Ph.D. degree in computer science from Cornell University.

For more information, you are welcome to visit his home page at http://research.microsoft.com/en-us/people/weic/.