Theory for local optima of nonconvex high-dimensional M-estimators

Add to your list(s) Download to your calendar using vCal

• Po-Ling Loh, University of Wisconsin - Madison
• Wednesday 21 February 2018, 14:00-15:00
• MR5 Centre for Mathematical Sciences.

If you have a question about this talk, please contact Rachel Furner.

We survey a variety of recent results concerning local optima of penalized M-estimators, designed for high-dimensional regression problems. The nonconvexity is allowed to arise in either the loss function or the regularizer. Although the overall landscape of the objective function is nonconvex in high dimensions, we show that both local and global optima are statistically consistent under appropriate conditions. Our theory is applicable to settings involving errors-in-variables models and other contaminated data scenarios. We also discuss statistical and optimization theory for nonconvex M-estimators suited for robust regression in high dimensions.

This talk is part of the CCIMI Seminars series.

This talk is included in these lists:

• All CMS events
• CCIMI
• CCIMI Seminars
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Guy Emerson's list
• Interested Talks
• MR5 Centre for Mathematical Sciences
• Trust & Technology Initiative - interesting events
• bld31
• custom
• ndk22's list
• rp587

Note that ex-directory lists are not shown.