Bandit Convex Optimisation
Will be published approx. on 28. February 2026
Book
Hardback
277 pages
978-1-009-60759-9 (ISBN)
Description
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, 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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Illustrations
Worked examples or Exercises
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 20 mm
Weight
560 gr
ISBN-13
978-1-009-60759-9 (9781009607599)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
Preface; 1. Introduction and problem statement; 2. Overview of methods and history; 3. Mathematical tools; 4. Bisection in one dimension; 5. Online gradient descent; 6. Self-concordant regularisation; 7. Linear and quadratic bandits; 8. Exponential weights; 9. Cutting plane methods; 10. Online Newton step; 11. Online Newton step for adversarial losses; 12. Gaussian optimistic smoothing; 13. Submodular minimisation; 14. Outlook; Appendix A. Miscellaneous; Appendix B. Concentration; Appendix C. Notation; Bibliography; Index.