J. Audibert, S. Bubeck, and R. Munos, Best arm identification in multi-armed bandits, Conference on Learning Theory, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00654404

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

P. Auer, R. Ortner, C. [. Szepesvári, R. Bubeck, S. Munos et al., Improved rates for the stochastic continuum-armed bandit problem Open loop optimistic planning Pure exploration in multi-armed bandits problems, 20th Conference on Learning Theory Conference on Learning Theory Proc. of the 20th International Conference on Algorithmic Learning Theory, pp.454-468, 2007.

L. Busoniu, R. Munos, B. D. Schutter, and R. Babuska, Optimistic planning for sparsely stochastic systems, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011.
DOI : 10.1109/ADPRL.2011.5967375

URL : https://hal.archives-ouvertes.fr/hal-00830125

S. Bubeck, R. Munos, G. Stoltz, and C. Szepesvári, Online optimization of X-armed bandits, Advances in Neural Information Processing Systems, pp.201-208, 2008.

S. Bubeck, R. Munos, G. Stoltz, C. [. Szepesvári, G. Bubeck et al., X-armed bandits Lipschitz bandits without the Lipschitz constant Bandit algorithms for tree search Convergence analysis of the direct algorithm, Proceedings of the 22nd International Conference on Algorithmic Learning Theory Uncertainty in Artificial IntelligenceFlo99] C.A. Floudas. Deterministic Global Optimization: Theory, Algorithms and ApplicationsGab01] J. M. X. Gablonsky. Modifications of the direct algorithm, pp.1655-1695, 1999.

S. Gelly, Y. Wang, R. Munos, O. R. Teytaudhan92-]-e, [. Hansen et al., Modification of UCT with patterns in monte-carlo go Global Optimization Using Interval Analysis Optimistic planning of deterministic systems Global Optimization ? Deterministic Approaches Lipschitzian optimization without the lipschitz constant Rigorous Global Search: Continuous Problems Nearly tight bounds for the continuum-armed bandit problem, European Workshop on Reinforcement Learning Springer LNAI 5323, editor, Recent Advances in Reinforcement Learning 18th Advances in Neural Information Processing SystemsKS06] L. Kocsis and Cs. Szepesvári. Bandit based Monte-Carlo planning Proceedings of the 15th European Conference on Machine Learning, pp.151-164157, 1992.

R. Kleinberg, A. Slivkins, and E. Upfal, Multi-armed bandits in metric spaces [Neu90] Neumaier. Interval Methods for Systems of Equations, Proceedings of the 40th ACM Symposium on Theory of ComputingPin96] J.D. Pintér. Global Optimization in Action (Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications, 1990.
DOI : 10.1145/1374376.1374475

URL : http://arxiv.org/abs/0809.4882

N. Srinivas, A. Krause, S. Kakade, M. Seegerss00, ]. R. Strongin et al., Gaussian process optimization in the bandit setting: No regret and experimental design [Sli11] A. Slivkins. Multi-armed bandits on implicit metric spaces Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms, International Conference on Machine Learning Advances in Neural Information Processing Systems, pp.1015-1022, 2000.