Bandit Convex Optimisation
Author:   Tor Lattimore (Google DeepMind, London)
Publisher:   Cambridge University Press
ISBN:  

9781009607599


Pages:   277
Publication Date:   28 February 2026
Format:   Hardback
Availability:   Not yet available   Availability explained
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Bandit Convex Optimisation


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Author:   Tor Lattimore (Google DeepMind, London)
Publisher:   Cambridge University Press
Imprint:   Cambridge University Press
ISBN:  

9781009607599


ISBN 10:   1009607596
Pages:   277
Publication Date:   28 February 2026
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

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.

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Author Information

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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