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Bandit Algorithms by Lattimore Tor (23 results)
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Bandit Algorithms
Language: English
Published by Cambridge University Press (edition 1), 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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.
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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New.
-
Bandit Algorithms (Hardback or Cased Book)
Language: English
Published by Cambridge University Press 9/10/2020, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Hardback or Cased Book. Condition: New. Bandit Algorithms. Book.
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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: As New. Unread book in perfect condition.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press CUP, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 51.83
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Bandit Algorithms
Language: English
Published by Cambridge University Press 2020-07-16, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Hardcover. Condition: New.
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Bandit Algorithms (Hardcover)
Language: English
Published by Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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 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. 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.
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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New. 2020. Hardcover. . . . . .
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Bandit Algorithms
Language: English
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
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, 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.
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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
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Hardcover. Condition: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock.
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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.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, GB, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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, 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.
-
Bandit Algorithms (Hardcover)
Language: English
Published by Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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 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. 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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Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New. Print on Demand.
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Hardcover. Condition: Brand New. 517 pages. 9.50x7.00x1.25 inches. In Stock. This item is printed on demand.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Condition: New. PRINT ON DEMAND.
-
Bandit Algorithms
Language: English
Published by Cambridge University Press, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 51.70
£ 22.95 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
-
Bandit Algorithms (Hardcover)
Language: English
Published by Cambridge University Press, Cambridge, 2020
ISBN 10: 1108486827 ISBN 13: 9781108486828
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 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
