000			01512nam a2200193 4500
008			220227b ||||| |||| 00| 0 eng d
020			_a9781108486828 _qhb.
082			_a519.3 _bLAT
100			_aLattimore, Tor. _9687
245			_aBandit algorithms
260			_aNew Delhi _bCambridge University Press _c2020
300			_axviii, 518p.,
520			_aDecision-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.
650			_aMathematical optimization _9688
650			_aProbabilities _9492
650			_aResource allocation--Mathematical models _9689
700			_aSzepesvari, Csaba. _9690
942			_cBK
999			_c7735 _d7735