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Sparse Accelerated Exponential Weights

Pierre Gaillard 1 Olivier Wintenberger 2, 3
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We consider the stochastic optimization problem where a convex function is minimized observing recursively the gradients. We introduce SAEW, a new procedure that accelerates exponential weights procedures with the slow rate 1/ √ T to procedures achieving the fast rate 1/T. Under the strong convexity of the risk, we achieve the optimal rate of convergence for approximating sparse parameters in R^d. The acceleration is achieved by using successive averaging steps in an online fashion. The procedure also produces sparse estimators thanks to additional hard threshold steps.
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Contributor : Pierre Gaillard <>
Submitted on : Monday, October 17, 2016 - 10:32:47 AM
Last modification on : Tuesday, August 4, 2020 - 3:46:49 AM
Document(s) archivé(s) le : Friday, February 3, 2017 - 4:16:54 PM


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  • HAL Id : hal-01376808, version 1


Pierre Gaillard, Olivier Wintenberger. Sparse Accelerated Exponential Weights. 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Apr 2017, Fort Lauderdale, United States. ⟨hal-01376808⟩



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