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Bandit Convex Optimisation
Brief Description
This comprehensive reference brings readers to the frontier of research on bandit convex optimisation or zeroth-order convex optimisation. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimisation and online learning, including gradient-based algorithms, interior point methods, cutting...
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Format: Hardback
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Bandit Convex Optimisation
$17800
Unit price
/
Unavailable
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.
A comprehensive reference for the theory of bandit convex optimization (zeroth-order optimisation) for researchers and students.
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Description
This comprehensive reference brings readers to the frontier of research on bandit convex optimisation or zeroth-order convex optimisation. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimisation and online learning, including gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery.
Bandit Convex Optimisation features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers.
Book Details
INFORMATION
ISBN: 9781009607599
Publisher: Cambridge University Press
Format: Hardback
Date Published: 19 March 2026
Country: United Kingdom
Imprint: Cambridge University Press
Illustration: Worked examples or Exercises
Audience: General / adult, Tertiary education, Professional and scholarly
DIMENSIONS
Weight: 0g
Pages: 280
About the Author
Tor Lattimore is a researcher at Google DeepMind working on reinforcement learning, bandits, optimisation and the theory of machine learning. He is the co-author of an introductory book on bandit algorithms and has published nearly 100 conference and journal articles. He is an action editor for the Journal of Machine Learning Research.
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