2013
NIPS
NeurIPS 2013
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
Abstract
Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batch setting that is often used in practice. Our main contribution is to introduce an accelerated mini-batch version of SDCA and prove a fast convergence rate for this method. We discuss an implementation of our method over a parallel computing system, and compare the results to both the vanilla stochastic dual coordinate ascent and to the accelerated deterministic gradient descent method of Nesterov [2007].
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Keyword Pioneer
— stochastic dual coordinate ascent
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Hot Topic Early Bird
— stochastic optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Mathematics & Optimization
Authors
Topics
Machine Learning > Optimization & Theory > Neural Network Optimization
Machine Learning > Optimization & Theory > Optimization
Mathematics & Optimization > Optimization > Optimization
Machine Learning > Core Methods > Optimization
Deep Learning > Optimization & Theory > Optimization
Machine Learning > Learning Types > Stochastic Methods