Improving confidence of dual averaging stochastic online learning via aggregation

Authors:
Sangkyun Lee
Affiliations:
Fakultät für Informatik, LS VIII, Technische Universität Dortmund, Dortmund, Germany
Venue:
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Year:
2012

Citing 2
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Primal-dual subgradient methods for convex problems

Mathematical Programming: Series A and B - Series B - Special Issue: Nonsmooth Optimization and Applications
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization

The Journal of Machine Learning Research

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Abstract

Stochastic online learning algorithms typically exhibit slow convergence speed, but their solutions of moderate accuracy often suffice in practice. Since the outcomes of these algorithms are random variables, not only their accuracy but also their probability of achieving a certain accuracy, called confidence, is important. We show that a rather simple aggregation of outcomes from parallel dual averaging runs can provide a solution with improved confidence, and it can be controlled by the number of runs, independently of the length of learning processes.