184 citations · 303 across the 5 of their papers we have counts for
4 papers · 1 filter
Learning Mixtures of Gaussians in High Dimensions
Rong Ge, Qingqing Huang, Sham M. Kakade
Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn,…
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
Rong Ge, Furong Huang, Chi Jin +1
We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main conc…
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders
Sanjeev Arora, Rong Ge, Ankur Moitra +1
We present a new algorithm for Independent Component Analysis (ICA) which has provable performance guarantees. In particular, suppose we are given samples of the form …
Learning Topic Models - Going beyond SVD
Sanjeev Arora, Rong Ge, Ankur Moitra
Topic Modeling is an approach used for automatic comprehension and classification of data in a variety of settings, and perhaps the canonical application is in uncovering thematic…