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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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