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Multi-armed bandit
Known as:
N-armed bandit
, Two-armed bandit
, K-armed bandit
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In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of…
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Related topics
Related topics
14 relations
A/B testing
Bayesian optimization
Design of experiments
Greedy algorithm
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Broader (2)
Machine learning
Stochastic optimization
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models
E. Kaufmann
,
O. Cappé
,
Aurélien Garivier
Journal of machine learning research
2014
Corpus ID: 12309216
The stochastic multi-armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics…
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Highly Cited
2010
Highly Cited
2010
Best Arm Identification in Multi-Armed Bandits
Jean-Yves Audibert
,
Sébastien Bubeck
,
R. Munos
Annual Conference Computational Learning Theory
2010
Corpus ID: 216050617
We consider the problem of finding the best arm in a stochastic multi-armed bandit game. The regret of a forecaster is here…
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Highly Cited
2010
Highly Cited
2010
A modern Bayesian look at the multi-armed bandit
S. L. Scott
2010
Corpus ID: 573750
A multi-armed bandit is an experiment with the goal of accumulating rewards from a payoff distribution with unknown parameters…
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Highly Cited
2009
Highly Cited
2009
Distributed Learning in Multi-Armed Bandit With Multiple Players
Keqin Liu
,
Qing Zhao
IEEE Transactions on Signal Processing
2009
Corpus ID: 16067339
We formulate and study a decentralized multi-armed bandit (MAB) problem. There are M distributed players competing for N…
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Highly Cited
2008
Highly Cited
2008
Learning diverse rankings with multi-armed bandits
Filip Radlinski
,
Robert D. Kleinberg
,
T. Joachims
International Conference on Machine Learning
2008
Corpus ID: 207168261
Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads…
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Highly Cited
2006
Highly Cited
2006
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
Eyal Even-Dar
,
Shie Mannor
,
Y. Mansour
Journal of machine learning research
2006
Corpus ID: 9715887
We incorporate statistical confidence intervals in both the multi-armed bandit and the reinforcement learning problems. In the…
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Highly Cited
2005
Highly Cited
2005
Multi-armed Bandit Algorithms and Empirical Evaluation
Joannès Vermorel
,
M. Mohri
European Conference on Machine Learning
2005
Corpus ID: 15607155
The multi-armed bandit problem for a gambler is to decide which arm of a K-slot machine to pull to maximize his total reward in a…
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Highly Cited
2002
Highly Cited
2002
Finite-time Analysis of the Multiarmed Bandit Problem
P. Auer
,
N. Cesa-Bianchi
,
P. Fischer
Machine-mediated learning
2002
Corpus ID: 207609497
Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring…
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Highly Cited
1998
Highly Cited
1998
Why Imitate, and If So, How?, : A Boundedly Rational Approach to Multi-armed Bandits
K. Schlag
1998
Corpus ID: 2714535
We consider the situation in which individuals in a finite population must repeatedly choose an action yielding an uncertain…
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Highly Cited
1995
Highly Cited
1995
Gambling in a rigged casino: The adversarial multi-armed bandit problem
P. Auer
,
N. Cesa-Bianchi
,
Y. Freund
,
R. Schapire
Proceedings of IEEE 36th Annual Foundations of…
1995
Corpus ID: 8963242
In the multi-armed bandit problem, a gambler must decide which arm of K non-identical slot machines to play in a sequence of…
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