P
Prateek Jain
Researcher at Google
Publications - 200
Citations - 12983
Prateek Jain is an academic researcher from Google. The author has contributed to research in topics: Matrix (mathematics) & Computer science. The author has an hindex of 51, co-authored 173 publications receiving 11698 citations. Previous affiliations of Prateek Jain include Microsoft & University of California, Berkeley.
Papers
More filters
Proceedings ArticleDOI
Information-theoretic metric learning
TL;DR: An information-theoretic approach to learning a Mahalanobis distance function that can handle a wide variety of constraints and can optionally incorporate a prior on the distance function and derive regret bounds for the resulting algorithm.
Proceedings ArticleDOI
Low-rank matrix completion using alternating minimization
TL;DR: This paper presents one of the first theoretical analyses of the performance of alternating minimization for matrix completion, and the related problem of matrix sensing, and shows that alternating minimizations guarantees faster convergence to the true matrix, while allowing a significantly simpler analysis.
Journal ArticleDOI
Phase Retrieval Using Alternating Minimization
TL;DR: In this paper, the authors show that a resampling version of the alternating minimization algorithm converges geometrically to the solution of a non-convex phase retrieval problem.
Proceedings Article
Guaranteed Rank Minimization via Singular Value Projection
TL;DR: Singular value projection (SVP) as discussed by the authors is a simple and fast algorithm for rank minimization under affine constraints (ARMP) and shows that SVP recovers the minimum rank solution for affine constraint that satisfy a restricted isometry property (RIP).
Posted Content
Guaranteed Rank Minimization via Singular Value Projection
TL;DR: Results show that the SVP-Newton method is significantly robust to noise and performs impressively on a more realistic power-law sampling scheme for the matrix completion problem.