Name
Title | ||
|---|---|---|
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 algorithmsu0027 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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Signal processing,Wireless,Regret,Toolbox,Algorithm,Statistical inference,Convex optimization,Big data,Hindsight bias,Mathematics |
DocType | Volume | Citations |
Journal | abs/1804.04529 | 0 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elena-Veronica Belmega | 1 | 159 | 19.91 |
| Panayotis Mertikopoulos | 2 | 258 | 43.71 |
| Romain Negrel | 3 | 33 | 3.42 |
| L. Sanguinetti | 4 | 1378 | 79.34 |