@inproceedings{2bbba96a578b479da5bbab8db6788789,
title = "Generalization error bounds for aggregation by mirror descent with averaging",
abstract = "We consider the problem of constructing an aggregated estimator from a finite class of base functions which approximately minimizes a convex risk functional under the ℓ 1 constraint. For this purpose, we propose a stochastic procedure, the mirror descent, which performs gradient descent in the dual space. The generated estimates are additionally averaged in a recursive fashion with specific weights. Mirror descent algorithms have been developed in different contexts and they are known to be particularly efficient in high dimensional problems. Moreover their implementation is adapted to the online setting. The main result of the paper is the upper bound on the convergence rate for the generalization error.",
author = "Anatoli Juditsky and Alexander Nazin and Alexandre Tsybakov and Nicolas Vayatis",
year = "2005",
month = dec,
day = "1",
language = "English",
isbn = "9780262232531",
series = "Advances in Neural Information Processing Systems",
pages = "603--610",
booktitle = "Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference",
note = "2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 ; Conference date: 05-12-2005 Through 08-12-2005",
}