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CORR
2011
Springer
167views Education» more  CORR 2011»
13 years 2 months ago
Fast global convergence of gradient methods for high-dimensional statistical recovery
Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
ICML
2007
IEEE
14 years 8 months ago
On one method of non-diagonal regularization in sparse Bayesian learning
In the paper we propose a new type of regularization procedure for training sparse Bayesian methods for classification. Transforming Hessian matrix of log-likelihood function to d...
Dmitry Kropotov, Dmitry Vetrov
AMC
2008
81views more  AMC 2008»
13 years 7 months ago
On Steepest-Descent-Kaczmarz methods for regularizing systems of nonlinear ill-posed equations
We investigate modified steepest descent methods coupled with a loping Kaczmarz strategy for obtaining stable solutions of nonlinear systems of ill-posed operator equations. We sh...
A. De Cezaro, Markus Haltmeier, Antonio Leit&atild...
ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 7 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
MP
2006
134views more  MP 2006»
13 years 7 months ago
Cubic regularization of Newton method and its global performance
In this paper, we provide theoretical analysis for a cubic regularization of Newton method as applied to unconstrained minimization problem. For this scheme, we prove general local...
Yurii Nesterov, Boris T. Polyak