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Online convex optimization and no-regret learning: Algorithms, guarantees and applications

Abstract : Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role. This trade-off is of particular importance to several branches and applications of signal processing, such as data mining, statistical inference, multimedia indexing and wireless communications (to name but a few). With this in mind, the aim of this tutorial paper is to provide a gentle introduction to online optimization and learning algorithms that are asymptotically optimal in hindsight - i.e., they approach the performance of a virtual algorithm with unlimited computational power and full knowledge of the future, a property known as no-regret. Particular attention is devoted to identifying the algorithms' theoretical performance guarantees and to establish links with classic optimization paradigms (both static and stochastic). To allow a better understanding of this toolbox, we provide several examples throughout the tutorial ranging from metric learning to wireless resource allocation problems.
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Contributor : Panayotis Mertikopoulos Connect in order to contact the contributor
Submitted on : Tuesday, October 9, 2018 - 5:01:19 PM
Last modification on : Tuesday, December 10, 2019 - 9:18:11 PM

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  • HAL Id : hal-01891562, version 1
  • ARXIV : 1804.04529




Elena Veronica Belmega, Panayotis Mertikopoulos, Romain Negrel, Sanguinetti Luca. Online convex optimization and no-regret learning: Algorithms, guarantees and applications. 2018. ⟨hal-01891562⟩



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