摘要：

Over the past years, variance reduced stochastic gradient methods have become increasingly popular, not only in the machine learning community, but also other areas including inverse problems and mathematical imaging to name a few. However, despite the varieties of variance reduced stochastic gradient descent methods, their analysis varies from each other. In this talk, I will first present a unified framework, under which we manage to abstract different variance reduced stochastic gradient methods into one. Then I will introduce a new stochastic method for composed optimization problems, and illustrate its performance via several imaging problems.

简介：

Research interests:  

Non-smooth Optimization, Mathematical Imaging and Data Science.  

Short bio:  

Tenure-track Associate Professor, Institute of Natural Science and School of Mathematical Sciences, Shanghai Jiao Tong University.

Lecturer in Mathematical Data Science (2020.11-2021.06), School of Mathematical Sciences, Queen Mary University of London.

Postdoc research associate (2017.04-2020.08), Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

Ph.D. in Mathematics (2016.10) GREYC, ENSICAEN and University of Caen Normandy.

M.S. in Mathematics (2013.03), School of Mathematics & Institute of Natural Sciences, Shanghai Jiao Tong University.

B.S. in Electrical & Information Engineering (2010.06), School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications.

摘要：

Over the past years, variance reduced stochastic gradient methods have become increasingly popular, not only in the machine learning community, but also other areas including inverse problems and mathematical imaging to name a few. However, despite the varieties of variance reduced stochastic gradient descent methods, their analysis varies from each other. In this talk, I will first present a unified framework, under which we manage to abstract different variance reduced stochastic gradient methods into one. Then I will introduce a new stochastic method for composed optimization problems, and illustrate its performance via several imaging problems.

简介：

Research interests:  

Non-smooth Optimization, Mathematical Imaging and Data Science.  

Short bio:  

Tenure-track Associate Professor, Institute of Natural Science and School of Mathematical Sciences, Shanghai Jiao Tong University.

Lecturer in Mathematical Data Science (2020.11-2021.06), School of Mathematical Sciences, Queen Mary University of London.

Postdoc research associate (2017.04-2020.08), Department of Applied Mathematics and Theoretical Physics, University of Cambridge.

Ph.D. in Mathematics (2016.10) GREYC, ENSICAEN and University of Caen Normandy.

M.S. in Mathematics (2013.03), School of Mathematics & Institute of Natural Sciences, Shanghai Jiao Tong University.

B.S. in Electrical & Information Engineering (2010.06), School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications.