Publication

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Martin Jaggi, Chenxin Ma
2021
Journal Articles

Résumé

Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent framework (CoCoA) and its improved variant CoCoA+ have been proposed, achieving a convergence rate of for solving empirical risk minimization problems with Lipschitz continuous losses. In this paper, an accelerated variant of CoCoA+ is proposed and shown to possess a convergence rate of in terms of reducing suboptimality. The analysis of this rate is also notable in that the convergence rate bounds involve constants that, except in extreme cases, are significantly reduced compared to those previously provided for CoCoA+. The results of numerical experiments are provided to show that acceleration can lead to significant performance gains.

Source officielle

https://infoscience.epfl.ch/entities/publication/ad7063cd-76da-4f3d-8576-f3fa1a1e0ed0

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Martin Jaggi, Chenxin Ma
2021
Journal Articles

Résumé

Source officielle

https://infoscience.epfl.ch/entities/publication/ad7063cd-76da-4f3d-8576-f3fa1a1e0ed0

À propos de ce résultat

Concepts associés (15)

Apprentissage supervisé

L'apprentissage supervisé (supervised learning en anglais) est une tâche d'apprentissage automatique consistant à apprendre une fonction de prédiction à partir d'exemples annotés, au contraire de l'apprentissage non supervisé. On distingue les problèmes de régression des problèmes de classement. Ainsi, on considère que les problèmes de prédiction d'une variable quantitative sont des problèmes de régression tandis que les problèmes de prédiction d'une variable qualitative sont des problèmes de classification.

Apprentissage automatique

L'apprentissage automatique (en anglais : machine learning, « apprentissage machine »), apprentissage artificiel ou apprentissage statistique est un champ d'étude de l'intelligence artificielle qui se fonde sur des approches mathématiques et statistiques pour donner aux ordinateurs la capacité d'« apprendre » à partir de données, c'est-à-dire d'améliorer leurs performances à résoudre des tâches sans être explicitement programmés pour chacune. Plus largement, il concerne la conception, l'analyse, l'optimisation, le développement et l'implémentation de telles méthodes.

Empirical risk minimization

Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on their performance. The core idea is that we cannot know exactly how well an algorithm will work in practice (the true "risk") because we don't know the true distribution of data that the algorithm will work on, but we can instead measure its performance on a known set of training data (the "empirical" risk).

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Understanding generalization and robustness in modern deep learning

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On the Generalization of Stochastic Gradient Descent with Momentum

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