The Landscape of Several Non-convex Statistical Learning Problems
发布时间：2017年09月11日
浏览次数：8277
发布者: Xiaoni Tan
主讲人： Song Mei (Stanford University)
活动时间： 从 2017-09-27 10:00 到 11:00
场地： 北京国际数学研究中心，全斋全9教室
For general non-convex optimization problems, local search algorithms such as gradient descent and trust region method could stuck at bad local minima and fail to find the global minimum. However in practice, local search algorithms work very well on a handful of non-convex statistical learning problems. To fill this gap between theory and practice, we propose to study the landscape (global geometry) of these statistical models. In this talk, I will go over several non-convex statistical models, as well as several powerful tools such as uniform convergence and Kac-Rice formula. I will show how these tools characterize the nice global geometry of these statistical models, and therefore local search algorithms work well provably.
