Principal Investigator Alexander Rakhlin
Non-Convex LandscapesHere we are interested in understanding properties of high-dimensional empirical landscapes that arise when one attempts to fit a model with many parameters (such as a multi-layer neural network or a latent variable model) to data. Some of the questions that arise are: (a) What is the behavior of optimization methods on such landscapes? (b) What salient features of the landscape arise from its random nature? (c) How can one exploit randomness in the optimization method to analyze its convergence?