Publication

Online Adaptive Methods, Universality and Acceleration

Volkan Cevher, Alp Yurtsever
2018
Conference Papers

Abstract

We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard convergence rate in the stochastic optimization setting. To the best of our knowledge, this is the first method that simultaneously applies to all of the above settings. At the heart of our method is an adaptive learning rate rule that employs importance weights, in the spirit of adaptive online learning algorithms, combined with an update that linearly couples two sequences. An empirical examination of our method demonstrates its applicability to the above mentioned scenarios and corroborates our theoretical findings.

Official source

https://infoscience.epfl.ch/entities/publication/c4fac84e-49f2-4035-8d5d-4ca7edc56918

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Volkan Cevher, Alp Yurtsever
2018
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c4fac84e-49f2-4035-8d5d-4ca7edc56918

About this result

Related concepts (15)

Online machine learning

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.

Stochastic gradient descent

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data).

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