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.
Complete list of metadatas

https://hal.inria.fr/hal-01891562
Contributor : Panayotis Mertikopoulos <>
Submitted on : Tuesday, October 9, 2018 - 5:01:19 PM
Last modification on : Tuesday, February 26, 2019 - 10:54:02 AM

Links full text

Identifiers

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

Collections

Citation

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

Share

Metrics

Record views

13