| Bandit Algorithms (Cambridge University Press) |
| Friday, 02 October 2020 | |||
Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. Tor Lattimore and Csaba Szepesvári focus on both mathematical intuition and carefully worked proofs. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. Author: Tor Lattimore and Csaba Szepesvári
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