274 citations · 3.1k across the 75 of their papers we have counts for
7 papers · 1 filter
Rich Component Analysis
Rong Ge, James Zou
In many settings, we have multiple data sets (also called views) that capture different and overlapping aspects of the same phenomenon. We are often interested in finding patterns…
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
Rong Ge, James Zou
Non-negative matrix factorization (NMF) is a natural model of admixture and is widely used in science and engineering. A plethora of algorithms have been developed to tackle NMF, b…
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization
Roy Frostig, Rong Ge, Sham M. Kakade +1
We develop a family of accelerated stochastic algorithms that minimize sums of convex functions. Our algorithms improve upon the fastest running time for empirical risk minimizatio…
Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms
Rong Ge, Tengyu Ma
Tensor rank and low-rank tensor decompositions have many applications in learning and complexity theory. Most known algorithms use unfoldings of tensors and can only handle rank up…
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…