{"id":171330,"date":"2014-04-04T06:19:02","date_gmt":"2014-04-04T06:19:02","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/project\/provable-non-convex-optimization-for-machine-learning-problems\/"},"modified":"2019-11-18T10:38:44","modified_gmt":"2019-11-18T18:38:44","slug":"provable-non-convex-optimization-for-machine-learning-problems","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/provable-non-convex-optimization-for-machine-learning-problems\/","title":{"rendered":"Provable Non-convex Optimization for Machine Learning Problems"},"content":{"rendered":"

In this work, we explore theoretical properties of simple non-convex optimization methods for problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc.<\/p>\n

Monographs<\/strong>:<\/p>\n