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Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration-exploitation trade-off.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
W. Thompson · 1933
Earlier work this paper cites.
Sequential Analysis
A. Wald · 1947
Earlier work this paper cites.
Bayes and minimax solutions of sequential decision problems
K.J. Arrow, D. Blackwell, and M.A. Girshick · 1949
Earlier work this paper cites.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Stochastic estimation of the maximum of a regression function
J. Kiefer and J. Wolfowitz · 1952
Earlier work this paper cites.
Some aspects of the sequential design of experiments
H. Robbins · 1952
Earlier work this paper cites.
Sequential minimax search for a maximum
J. Kiefer · 1953
Earlier work this paper cites.
Approximation to Bayes risk in repeated play
J. Hannan · 1957
Earlier work this paper cites.
On pseudo-games
A. Baños · 1968
Earlier work this paper cites.
Bandit processes and dynamic allocation indices
J.C. Gittins · 1979
Earlier work this paper cites.
Efficient methods for large-scale convex optimization problems
A. Nemirovski · 1979
Earlier work this paper cites.
Problem Complexity and Method Efficiency in Optimization
A. Nemirovski and D. Yudin · 1983
Earlier work this paper cites.
Asymptotically efficient adaptive allocation rules
T. L. Lai and H. Robbins · 1985
Earlier work this paper cites.
Sample mean based index policies with 𝒪 ( log n ) \mathcal{O}(\log n) regret for the multi-armed bandit problem
R. Agrawal · 1995
Earlier work this paper cites.
An elementary introduction to modern convex geometry
K. Ball · 1997
Earlier work this paper cites.
Bandit problems with infinitely many arms
D.A. Berry, R.W. Chen, D.C. Heath A. Zame, and L.A. Shepp · 1997
Earlier work this paper cites.
Optimal adaptive policies for Markov decision processes
A.N. Burnetas and M.N. Katehakis · 1997
Earlier work this paper cites.
Some label efficient learning results
D. Helmbold and S. Panizza · 1997
Earlier work this paper cites.
Asymptotic calibration
D. Foster and R. Vohra · 1998
Earlier work this paper cites.
Tracking the best expert
M. Herbster and M. Warmuth · 1998
Earlier work this paper cites.
Chernoff-type bound for finite Markov chains
P. Lezaud · 1998
Earlier work this paper cites.
Reinforcement Learning: An Introduction
R.S. Sutton and A.G. Barto · 1998
Earlier work this paper cites.
Associative reinforcement learning using linear probabilistic concepts
N. Abe and P.M. Long · 1999
Earlier work this paper cites.
The conjugate barrier Mirror Descent method for non-smooth convex optimization
A. Ben-Tal and A. Nemirovski · 1999
Earlier work this paper cites.
A simple adaptive procedure leading to correlated equilibrium
S. Hart and A. Mas-Colell · 2000
Earlier work this paper cites.
General convergence results for linear discriminant updates
A. Grove, N. Littlestone, and D. Schuurmans · 2001
Earlier work this paper cites.
A general class of adaptive strategies
S. Hart and A. Mas-Colell · 2001
Earlier work this paper cites.
Fundamentals of Convex Analysis
J.-B. Hiriart-Urruty and C. Lemaréchal · 2001
Earlier work this paper cites.
Relative loss bounds for multidimensional regression problems
J. Kivinen and M. Warmuth · 2001
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
P. Auer · 2002
Earlier work this paper cites.
Pac bounds for multi-armed bandit and markov decision processes
E. Even-Dar, S. Mannor, and Y. Mansour · 2002
Earlier work this paper cites.
A sparse sampling algorithm for near-optimal planning in large Markovian decision processes
M. Kearns, Y. Mansour, and A.Y. Ng · 2002
Earlier work this paper cites.
Mirror Descent and nonlinear projected subgradient methods for convex optimization
A. Beck and M. Teboulle · 2003
Earlier work this paper cites.
On the Sample Complexity of Reinforcement Learning
S.M. Kakade · 2003
Earlier work this paper cites.
Paths kernels and multiplicative updates
E. Takimoto and M. Warmuth · 2003
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
M. Zinkevich · 2003
Earlier work this paper cites.
Adaptive routing with end-to-end feedback: distributed learning and geometric approaches
B. Awerbuch and R. Kleinberg · 2004
Earlier work this paper cites.
Convex Optimization
S. Boyd and L. Vandenberghe · 2004
Earlier work this paper cites.
Nearly tight bounds for the continuum-armed bandit problem
R. Kleinberg · 2004
Earlier work this paper cites.
The sample complexity of exploration in the multi-armed bandit problem
S. Mannor and J. N. Tsitsiklis · 2004
Earlier work this paper cites.
Online geometric optimization in the bandit setting against an adaptive adversary
H. McMahan and A. Blum · 2004
Earlier work this paper cites.
Theory of classification: a survey of recent advances
S. Boucheron, O. Bousquet, and G. Lugosi · 2005
Earlier work this paper cites.
Minimizing regret with label efficient prediction
N. Cesa-Bianchi, G. Lugosi, and G. Stoltz · 2005
Earlier work this paper cites.
Online convex optimization in the bandit setting: Gradient descent without a gradient
A. Flaxman, A. Kalai, and B. McMahan · 2005
Earlier work this paper cites.
Recursive aggregation of estimators by the Mirror Descent algorithm with averaging
A. Juditsky, A. Nazin, A. Tsybakov, and N. Vayatis · 2005
Earlier work this paper cites.
Efficient algorithms for online decision problems
A. Kalai and S. Vempala · 2005
Earlier work this paper cites.
Incomplete Information and Internal Regret in Prediction of Individual Sequences
G. Stoltz · 2005
Earlier work this paper cites.
Hannan consistency in on-line learning in case of unbounded losses under partial monitoring
C. Allenberg, P. Auer, L. Györfi, and G. Ottucsák · 2006
Earlier work this paper cites.
Prediction, Learning, and Games
N. Cesa-Bianchi and G. Lugosi · 2006
Earlier work this paper cites.
Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
E. Even-Dar, S. Mannor, and Y. Mansour · 2006
Earlier work this paper cites.
Modification of UCT with patterns in Monte-Carlo Go
S. Gelly, Y. Wang, R. Munos, and O. Teytaud · 2006
Earlier work this paper cites.
Bandit based Monte-Carlo planning
L. Kocsis and C. Szepesvári · 2006
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Combining expert advice in reactive environments
D. Pucci de Farias and N. Megiddo · 2006
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Improved rates for the stochastic continuum-armed bandit problem
P. Auer, R. Ortner, and C. Szepesvári · 2007
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Improved second-order bounds for prediction with expert advice
N. Cesa-Bianchi, Y. Mansour, and G. Stoltz · 2007
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Bandit algorithms for tree search
P.-A. Coquelin and R. Munos · 2007
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Continuous time associative bandit problems
A. György, L. Kocsis, I. Szabó, and C. Szepesvári · 2007
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A contextual-bandit approach to personalized news article recommendation
L. Li, W. Chu, J. Langford, and R.E. Schapire · 2010
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Dynamic multichannel access with imperfect channel state detection
K. Liu, Q. Zhao, and B. Krishnamachari · 2010
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Online Markov decision processes under bandit feedback
G. Neu, A. Gyorgy, C. Szepesvari, and A. Antos · 2010
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Linearly parameterized bandits
P. Rusmevichientong and J. Tsitsiklis · 2010
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Gaussian process optimization in the bandit setting: no regret and experimental design
N. Srinivas, A. Krause, S.M. Kakade, and M. Seeger · 2010
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Algorithms for Reinforcement Learning
C. Szepesvári · 2010
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A. György, T. Linder, G. Lugosi, and G. Ottucsák · 2007
Cited alongside, same era.
The epoch-greedy algorithm for contextual multi-armed bandits
J. Langford and T. Zhang · 2007
Cited alongside, same era.
Online Learning: Theory, Algorithms, and Applications
S. Shalev-Shwartz · 2007
Cited alongside, same era.
Competing in the dark: An efficient algorithm for bandit linear optimization
J. Abernethy, E. Hazan, and A. Rakhlin · 2008
Cited alongside, same era.
Active learning in multi-armed bandits
A. Antos, V. Grover, and C. Szepesvári · 2008
Cited alongside, same era.
High probability regret bounds for online optimization
P. Bartlett, V. Dani, T. Hayes, S. Kakade, A. Rakhlin, and A. Tewari · 2008
Cited alongside, same era.
Algorithms for adversarial bandit problems with multiple plays
T. Uchiya, A. Nakamura, and M. Kudo · 2010
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Improved algorithms for linear stochastic bandits
Y. Abbasi-Yadkori, D. Pal, and C. Szepesvári · 2011
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Bandits, query learning, and the haystack dimension
K. Amin, M. Kearns, and U. Syed · 2011
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Minimax policies for combinatorial prediction games
J.-Y. Audibert, S. Bubeck, and G. Lugosi · 2011
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Non-asymptotic analysis of stochastic approximation algorithms for machine learning
F. Bach and E. Moulines · 2011
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Online learning
G. Bartok, D. Pal, C. Szepesvári, and I. Szita · 2011
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Introduction to online optimization
S. Bubeck · 2011
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Committing bandits
L. Bui, R. Johari, and S. Mannor · 2011
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Fast boosting using adversarial bandits
R. Busa-Fekete and B. Kegl · 2011
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Finite time analysis of stratified sampling for monte carlo
A. Carpentier and R. Munos · 2011
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Upper confidence bounds algorithms for active learning in multi-armed bandits
A. Carpentier, A. Lazaric, M. Ghavamzadeh, R. Munos, and P. Auer · 2011
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An empirical evaluation of Thompson sampling
O. Chapelle and L. Li · 2011
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Contextual bandits with linear payoff functions
W. Chu, L. Li, L. Reyzin, and R. Schapire · 2011
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Multiclass classification with bandit feedback using adaptive regularization
K. Crammer and C. Gentile · 2011
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Efficient optimal learning for contextual bandits
M. Dudik, D. Hsu, S. Kale, N. Karampatziakis, J. Langford, L. Reyzin, and T. Zhang · 2011
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Optimally sensing a single channel without prior information: The tiling algorithm and regret bounds
S. Filippi, O. Cappé, and A. Garivier · 2011
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Multi-bandit best arm identification
V. Gabillon, M. Ghavamzadeh, A. Lazaric, and S. Bubeck · 2011
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The KL-UCB algorithm for bounded stochastic bandits and beyond
A. Garivier and O. Cappé · 2011
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On upper-confidence bound policies for switching bandit problems
A. Garivier and E. Moulines · 2011
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Multi-Armed Bandit Allocation Indices (2nd edition)
J. Gittins, K. Glazebrook, and R. Weber · 2011
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The convex optimization approach to regret minimization
E. Hazan · 2011
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NEWTRON: an efficient bandit algorithm for online multiclass prediction
E. Hazan and S. Kale · 2011
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Adaptive bandits: Towards the best history-dependent strategy
O. Maillard and R. Munos · 2011
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A finite-time analysis of multi-armed bandits problems with Kullback-Leibler divergences
O.-A. Maillard, R. Munos, and G. Stoltz · 2011
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From bandits to experts: On the value of side-observations
S. Mannor and O. Shamir · 2011
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Random gradient-free minimization of convex functions
Y. Nesterov · 2011
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The multi-armed bandit problem with covariates
V. Perchet and P. Rigollet · 2011
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Deviations of stochastic bandit regret
A. Salomon and J.-Y. Audibert · 2011
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Pac-bayesian analysis of contextual bandits
Y. Seldin, P. Auer, F. Laviolette, J. Shawe-Taylor, and R. Ortner · 2011
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Contextual bandits with similarity information
A. Slivkins · 2011
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On the universality of online Mirror Descent
N. Srebro, K. Sridharan, and A. Tewari · 2011
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Combining initial segments of lists
M. Warmuth, W. Koolen, and D. Helmbold · 2011
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Unimodal bandits
J.Y. Yu and S. Mannor · 2011
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Beat the mean bandit
Y. Yue and T. Joachims · 2011
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Analysis of Thompson sampling for the multi-armed bandit problem
S. Agrawal and N. Goyal · 2012
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The best of both worlds: stochastic and adversarial bandits
S. Bubeck and A. Slivkins · 2012
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Kullback-Leibler upper confidence bounds for optimal sequential allocation
O. Cappé, A. Garivier, O. Maillard, R. Munos, and G. Stoltz · 2012
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Combinatorial bandits
N. Cesa-Bianchi and G. Lugosi · 2012
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No internal regret via neighborhood watch
D. Foster and A. Rakhlin · 2012
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Regularization techniques for learning with matrices
S. Kakade, S. Shalev-Shwartz, and A. Tewari · 2012
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Learning hurdles for sleeping experts
V. Kanade and T. Steinke · 2012
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Regret bounds for restless Markov bandits
R. Ortner, D. Ryabko, P. Auer, and R. Munos · 2012
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Online learning of rested and restless bandits
C. Tekin and M. Liu · 2012
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Single-call mechanisms
Christopher A. Wilkens and Balasubramanian Sivan · 2012
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