000			03751cam a22004218i 4500
001			21440656
003			BD-ChCU
005			20240324102125.0
008			200208s2020 enk b 001 0 eng
010			_a 2019053276
020			_a9781108486828 _q(hardback)
020			_z9781108571401 _q(epub)
040			_aLBSOR/DLC _beng _erda _cDLC
042			_apcc
050	0	0	_aQA402.5 _b.L367 2020
082	0	0	_a519.3 L351 b _223
100	1		_aLattimore, Tor, _d1987- _eauthor.
245	1	0	_aBandit algorithms / _cTor Lattimore and Csaba Szepesvari.
263			_a2005
264		1	_aCambridge ; _aNew York, NY : _bCambridge University Press, _c2020.
300			_apages cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aFoundations of probability -- Stochastic processes and Markov chains -- Stochastic bandits -- Concentration of measure -- The explore-then-commit algorithm -- The upper confidence bound algorithm -- The upper confidence bound algorithm: asymptotic optimality -- The upper confidence bound algorithm: minimax optimality -- The upper confidence bound algorithm: Bernoulli noise -- The Exp3 algorithm -- The Exp3-IX algorithm -- Lower bounds: basic ideas -- Foundations of information theory -- Minimax lower bounds -- Instance dependent lower bounds -- High probability lower bounds -- Contextual bandits -- Stochastic linear bandits -- Confidence bounds for least squares estimators -- Optimal design for least squares estimators -- Stochastic linear bandits with finitely many arms -- Stochastic linear bandits with sparsity -- Minimax lower bounds for stochastic linear bandits -- Asymptotic lower bounds for stochastic linear bandits -- Foundations of convex analysis -- Exp3 for adversarial linear bandits -- Follow the regularized leader and mirror descent -- The relation between adversarial and stochastic linear bandits -- Combinatorial bandits -- Non-stationary bandits -- Ranking -- Pure exploration -- Foundations of Bayesian learning -- Bayesian bandits -- Thompson sampling -- Partial monitoring -- Markov decision processes.
520			_a"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"-- _cProvided by publisher.
650		0	_aMathematical optimization.
650		0	_aProbabilities.
650		0	_aDecision making _xMathematical models.
650		0	_aResource allocation _xMathematical models.
650		0	_aAlgorithms.
700	1		_aSzepesvári, Csaba, _eauthor.
776	0	8	_iOnline version: _aLattimore, Tor, 1987- _tBandit algorithms _dCambridge ; New York, NY : Cambridge University Press, 2020 _z9781108571401 _w(DLC) 2019053277
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _n0
999			_c103924 _d103924