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NIPS
2004
14 years 8 days ago
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices
We prove generalization error bounds for predicting entries in a partially observed matrix by fitting the observed entries with a low-rank matrix. In justifying the analysis appro...
Nathan Srebro, Noga Alon, Tommi Jaakkola
CORR
2011
Springer
202views Education» more  CORR 2011»
13 years 5 months ago
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
We analyze a class of estimators based on a convex relaxation for solving highdimensional matrix decomposition problems. The observations are the noisy realizations of the sum of ...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
COLT
2005
Springer
14 years 4 months ago
Rank, Trace-Norm and Max-Norm
We study the rank, trace-norm and max-norm as complexity measures of matrices, focusing on the problem of fitting a matrix with matrices having low complexity. We present generali...
Nathan Srebro, Adi Shraibman
VR
2010
IEEE
154views Virtual Reality» more  VR 2010»
13 years 9 months ago
On error bound estimation for motion prediction
A collaborative virtual environment (CVE) allows remote users to access and modify shared data through networks, such as the Internet. However, when the users are connected via th...
Rynson W. H. Lau, Kenneth Lee
ICCAD
2005
IEEE
106views Hardware» more  ICCAD 2005»
14 years 4 months ago
Fast balanced stochastic truncation via a quadratic extension of the alternating direction implicit iteration
— Balanced truncation (BT) model order reduction (MOR) is known for its superior accuracy and computable error bounds. Balanced stochastic truncation (BST) is a particular BT pro...
Ngai Wong, Venkataramanan Balakrishnan