Lattimore Tor (41 results)
Language: English
Published by Cambridge University Press (edition 1), 2020
- Hardcover
Seller: BooksRun, Philadelphia, PA, U.S.A.BooksRun
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US$ 44.51
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Hardcover. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
- Hardcover
Seller: Books From California, Simi Valley, CA, U.S.A.Books From California
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US$ 54.54
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hardcover. Condition: Very Good.
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Hardcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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US$ 67.46
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Hardback or Cased Book. Condition: New. Bandit Algorithms. Book.
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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US$ 69.00
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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US$ 71.40
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Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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US$ 75.00
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- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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US$ 75.36
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Condition: New.
- Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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US$ 81.08
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Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: Chiron Media, Wallingford, United KingdomChiron Media
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US$ 66.01
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Hardcover. Condition: New.
- Hardcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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US$ 76.56
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Condition: New. 2020. Hardcover. . . . . .
- Hardcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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US$ 84.86
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Hardcover. Condition: new. Hardcover. 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 st…ochastic, 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. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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US$ 71.67
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Condition: New. In.
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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US$ 69.45
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Condition: New.
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- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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US$ 87.25
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Hardback. Condition: New. 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, ad…versarial, 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.
- Hardcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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US$ 87.48
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Condition: New. 2026. hardcover. . . . . .
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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US$ 78.21
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Condition: New.
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- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
Contact seller5-star sellerCondition: Used - As new
US$ 78.89
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Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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US$ 92.10
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Condition: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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US$ 84.90
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Condition: As New. Unread book in perfect condition.
- Hardcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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US$ 102.04
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Condition: New. 2026. hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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US$ 112.74
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Condition: New.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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US$ 102.69
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Hardcover. Condition: Brand New. 280 pages. 6.00x0.69x9.00 inches. In Stock.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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US$ 100.53
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Hardcover. Condition: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock.
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- Hardcover
Seller: moluna, Greven, Germanymoluna
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US$ 76.81
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Condition: New. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for gra.
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- Hardcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
US$ 83.57
US$ 87.28 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Hardback. Condition: New. 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, ad…versarial, 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.
- Hardcover
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
US$ 133.31
US$ 37.00 shippingShips from Australia to U.S.A.Quantity: 1 available
Hardcover. Condition: new. Hardcover. 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 st…ochastic, 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. Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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US$ 111.55
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This comprehensive reference brings readers to the frontier of research on bandit convex optimization or zeroth-order convex optimization. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from conve…x optimization and online learning, including gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery. The book features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers.
- Hardcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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US$ 73.58
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Hardcover. Condition: new. Hardcover. This comprehensive reference brings readers to the frontier of research on bandit convex optimization or zeroth-order convex optimization. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimization and online learning, i…ncluding gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery. The book features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers. This self-contained reference brings readers to the frontier of research on bandit convex optimization while presenting fundamental tools from convex optimization such as gradient-based algorithms, interior point methods and cutting plane methods. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
