Bandit Algorithms

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 to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. 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. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

Author: Tor Lattimore, Csaba Szepesvári
Publisher: Cambridge University Press
Published: 09/10/2020
Pages: 536
Binding Type: Hardcover
Weight: 2.30lbs
Size: 9.80h x 7.70w x 1.30d
ISBN13: 9781108486828
ISBN10: 1108486827
BISAC Categories:
- Computers | Artificial Intelligence | Computer Vision & Pattern Recognit
- Mathematics | Game Theory