Shenjian Zhao

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
In many learning tasks, structural models usually lead to better interpretability and higher generalization performance. In recent years, however, the simple structural models such as lasso are frequently proved to be insufficient. Accordingly, there has been a lot of work on " superposition-structured " models where multiple structural constraints are(More)
We develop a generalized proximal quasi-Newton method for handling " dirty " statistical models where multiple structural constraints are imposed. We consider a general class of M-estimators that minimize the sum of a smooth loss function and a hybrid regularization. We show that the generalized proximal quasi-Newton method inherits the superlinear(More)
  • 1