Lattimore Tor-Bandit Algorithms HBOOK NEW Najnowszy standard produktu

Lattimore Tor-Bandit Algorithms HBOOK NEW Najnowszy standard produktu

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Ostatnia aktualizacja: 16-10-2021 06:56:47 CEST Wyświetl wszystkie poprawki

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Stan: Nowy: Nowa, nieczytana, nieużywana książka w idealnym stanie, wszystkie strony, bez uszkodzeń. Aby poznać ... Dowiedz się więcejo stanie przedmiotu Artist: Lattimore Tor
Author: Csaba Szepesvári, Tor Lattimore EAN: 9781108486828
Movie/TV Title: Bandit Algorithms ISBN: 9781108486828
Release Title: Bandit Algorithms Publication Name: Bandit Algorithms
Book Title: Bandit Algorithms Publication Year: 2020
Type: Textbook Format: Hardcover
Language: English Item Height: 1.3in.
Item Length: 9.9in. Publisher: Cambridge University Press
Item Width: 7.2in. Item Weight: 37.7 Oz
Number of Pages: 536 Pages

O tym produkcie

Product Information
Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.
Product Identifiers
PublisherCambridge University Press
ISBN-101108486827
ISBN-139781108486828
eBay Product ID (ePID)8038912590
Product Key Features
AuthorCsaba Szepesvári, Tor Lattimore
Publication NameBandit Algorithms
FormatHardcover
LanguageEnglish
Publication Year2020
TypeTextbook
Number of Pages536 Pages
Dimensions
Item Length9.9in.
Item Height1.3in.
Item Width7.2in.
Item Weight37.7 Oz
Additional Product Features
Lc Classification NumberQa402.5.L367 2020
Reviews'This year marks the 68th anniversary of 'multi-armed bandits' introduced by Herbert Robbins in 1952, and the 35th anniversary of his 1985 paper with me that advanced multi-armed bandit theory in new directions via the concept of 'regret' and a sharp asymptotic lower bound for the regret. This vibrant subject has attracted important multidisciplinary developments and applications. Bandit Algorithms gives it a comprehensive and up-to-date treatment, and meets the need for such books in instruction and research in the subject, as in a new course on contextual bandits and recommendation technology that I am developing at Stanford.' Tze L. Lai, Stanford University
Table of Content1. Introduction; 2. Foundations of probability; 3. Stochastic processes and Markov chains; 4. Finite-armed stochastic bandits; 5. Concentration of measure; 6. The explore-then-commit algorithm; 7. The upper confidence bound algorithm; 8. The upper confidence bound algorithm: asymptotic optimality; 9. The upper confidence bound algorithm: minimax optimality; 10. The upper confidence bound algorithm: Bernoulli noise; 11. The Exp3 algorithm; 12. The Exp3-IX algorithm; 13. Lower bounds: basic ideas; 14. Foundations of information theory; 15. Minimax lower bounds; 16. Asymptotic and instance dependent lower bounds; 17. High probability lower bounds; 18. Contextual bandits; 19. Stochastic linear bandits; 20. Confidence bounds for least squares estimators; 21. Optimal design for least squares estimators; 22. Stochastic linear bandits with finitely many arms; 23. Stochastic linear bandits with sparsity; 24. Minimax lower bounds for stochastic linear bandits; 25. Asymptotic lower bounds for stochastic linear bandits; 26. Foundations of convex analysis; 27. Exp3 for adversarial linear bandits; 28. Follow the regularized leader and mirror descent; 29. The relation between adversarial and stochastic linear bandits; 30. Combinatorial bandits; 31. Non-stationary bandits; 32. Ranking; 33. Pure exploration; 34. Foundations of Bayesian learning; 35. Bayesian bandits; 36. Thompson sampling; 37. Partial monitoring; 38. Markov decision processes.
Copyright Date2020
Target AudienceScholarly & Professional
TopicGeneral, Computer Vision & Pattern Recognition
Lccn2019-053276
Dewey Decimal519.3
Dewey Edition23
GenreComputers, Mathematics

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