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Author: Tor Lattimore
Publisher: Cambridge University Press
Published: 2020-07-16
Total Pages: 537
ISBN-13: 1108486827
DOWNLOAD EBOOKA comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
Author: Aleksandrs Slivkins
Publisher:
Published: 2019-10-31
Total Pages: 306
ISBN-13: 9781680836202
DOWNLOAD EBOOKMulti-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first book to provide a textbook like treatment of the subject.
Author: John Myles White
Publisher: "O'Reilly Media, Inc."
Published: 2012-12-10
Total Pages: 88
ISBN-13: 1449341586
DOWNLOAD EBOOKWhen looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website. Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms Develop a unit testing framework for debugging bandit algorithms Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials
Author: Vicki Hearne
Publisher: Skyhorse Publishing Inc.
Published: 2007-08-17
Total Pages: 376
ISBN-13: 1602390703
DOWNLOAD EBOOK"Learned and brilliant and wonderful."Wall Street...
Author: Barry Cole
Publisher: barry cole
Published: 2018-02-26
Total Pages: 66
ISBN-13: 0993583156
DOWNLOAD EBOOKThe story follows the adventures of two eleven year old children Lizzie and Sam when they discover a one-arm-bandit in an scrap yard where they had gone to hide from PC Goodrich. Eager to see if the machine worked, Sam pulls on the lever and, in the blink of an eye they are transported back in time to the burial of a Saxon King at Sutton Hoo. Relieved to find themselves safely returned to the scrap yard. their joy is short lived when they find PC Goodrich waiting there to apprehend them. But when the policeman spots the one arm bandit, unable to resist the temptation, PC Goodrich pulls down the handle. Instantly the trio are transported back in time to the banks of the Little Bighorn River in Montana at the very time that General Custer is making his famous last stand. With the seventh cavalry and the Indians locked in battle as it reaches its climax, before PC Goodrich can stop her, Lizzie suddenly rushes onto the battle-field, and without a thought for her own safety, she saves the life of a cavalry officers horse. Safely back in the scrap yard, thanks to the policeman's knowledge of American history, Lizzie learns that because of her bravery, the horse who's life she saved, would turn out to be the only survivor of the battle. But sadly there were to be no more time travel adventures, as a few weeks later the scrap yard was demolished, burying the one arm bandit under mounds of rubble. At least that's what Lizzie and Sam thought. But they were wrong. Thanks to a canny Scot named Hamish McGregor and his trusty old Transit van, the one arm bandit was in fact on its way up the A1 to Scotland.
Author: Donald A. Berry
Publisher: Springer Science & Business Media
Published: 2013-04-17
Total Pages: 283
ISBN-13: 9401537119
DOWNLOAD EBOOKOur purpose in writing this monograph is to give a comprehensive treatment of the subject. We define bandit problems and give the necessary foundations in Chapter 2. Many of the important results that have appeared in the literature are presented in later chapters; these are interspersed with new results. We give proofs unless they are very easy or the result is not used in the sequel. We have simplified a number of arguments so many of the proofs given tend to be conceptual rather than calculational. All results given have been incorporated into our style and notation. The exposition is aimed at a variety of types of readers. Bandit problems and the associated mathematical and technical issues are developed from first principles. Since we have tried to be comprehens ive the mathematical level is sometimes advanced; for example, we use measure-theoretic notions freely in Chapter 2. But the mathema tically uninitiated reader can easily sidestep such discussion when it occurs in Chapter 2 and elsewhere. We have tried to appeal to graduate students and professionals in engineering, biometry, econ omics, management science, and operations research, as well as those in mathematics and statistics. The monograph could serve as a reference for professionals or as a telA in a semester or year-long graduate level course.
Author: Sébastien Bubeck
Publisher: Now Pub
Published: 2012
Total Pages: 138
ISBN-13: 9781601986269
DOWNLOAD EBOOKIn this monograph, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it analyzes some of the most important variants and extensions, such as the contextual bandit model.
Author: United States. Congress. House. Committee on Interstate and Foreign Commerce
Publisher:
Published: 1950
Total Pages: 320
ISBN-13:
DOWNLOAD EBOOKAuthor: Yoav Freund
Publisher: Springer Science & Business Media
Published: 2008-09-29
Total Pages: 480
ISBN-13: 3540879862
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.
Author: Djallel Bouneffouf
Publisher: Djallel Bouneffouf
Published: 2023-03-14
Total Pages: 234
ISBN-13:
DOWNLOAD EBOOKIn recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning from less feedback. The multiarmed bandit field is currently experiencing a renaissance, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This book aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.
