Tor Lattimore, Csaba Szepesvari

Bandit Algorithms (eBook, PDF)

• Format: PDF

Produktbeschreibung

A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 16. Juli 2020
• Englisch
• ISBN-13: 9781108687492
• Artikelnr.: 70910799

Autorenporträt

Tor Lattimore is a research scientist at DeepMind. His research is focused on decision making in the face of uncertainty, including bandit algorithms and reinforcement learning. Before joining DeepMind he was an assistant professor at Indiana University and a postdoctoral fellow at the University of Alberta.

Inhaltsangabe

1. Introduction
2. Foundations of probability
3. Stochastic processes and Markov chains
4. Finite-armed stochastic bandits
5. Concentration of measure
6. The explore-then-commit algorithm
7. The upper confidence bound algorithm
8. The upper confidence bound algorithm: asymptotic optimality
9. The upper confidence bound algorithm: minimax optimality
10. The upper confidence bound algorithm: Bernoulli noise
11. The Exp3 algorithm
12. The Exp3-IX algorithm
13. Lower bounds: basic ideas
14. Foundations of information theory
15. Minimax lower bounds
16. Asymptotic and instance dependent lower bounds
17. High probability lower bounds
18. Contextual bandits
19. Stochastic linear bandits
20. Confidence bounds for least squares estimators
21. Optimal design for least squares estimators
22. Stochastic linear bandits with finitely many arms
23. Stochastic linear bandits with sparsity
24. Minimax lower bounds for stochastic linear bandits
25. Asymptotic lower bounds for stochastic linear bandits
26. Foundations of convex analysis
27. Exp3 for adversarial linear bandits
28. Follow the regularized leader and mirror descent
29. The relation between adversarial and stochastic linear bandits
30. Combinatorial bandits
31. Non-stationary bandits
32. Ranking
33. Pure exploration
34. Foundations of Bayesian learning
35. Bayesian bandits
36. Thompson sampling
37. Partial monitoring
38. Markov decision processes.