274 citations · 3.1k across the 75 of their papers we have counts for
6 papers · 1 filter
Provable learning of Noisy-or Networks
Sanjeev Arora, Rong Ge, Tengyu Ma +1
Many machine learning applications use latent variable models to explain structure in data, whereby visible variables (= coordinates of the given datapoint) are explained as a prob…
Homotopy Analysis for Tensor PCA
Anima Anandkumar, Yuan Deng, Rong Ge +1
Developing efficient and guaranteed nonconvex algorithms has been an important challenge in modern machine learning. Algorithms with good empirical performance such as stochastic g…
Provable Algorithms for Inference in Topic Models
Sanjeev Arora, Rong Ge, Frederic Koehler +2
Recently, there has been considerable progress on designing algorithms with provable guarantees -- typically using linear algebraic methods -- for parameter learning in latent vari…
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
Rong Ge, Chi Jin, Sham M. Kakade +2
This paper considers the problem of canonical-correlation analysis (CCA) (Hotelling, 1936) and, more broadly, the generalized eigenvector problem for a pair of symmetric matrices.…
Matrix Completion has No Spurious Local Minimum
Rong Ge, Jason D. Lee, Tengyu Ma
Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algo…
Efficient approaches for escaping higher order saddle points in non-convex optimization
Anima Anandkumar, Rong Ge
Local search heuristics for non-convex optimizations are popular in applied machine learning. However, in general it is hard to guarantee that such algorithms even converge to a lo…