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

. Bubeck, . Sébastien, . Munos, . Rémi, . Stoltz et al., Online Optimization of X-armed Bandits, Advances in Neural Information Processing Systems, pp.201-208, 2008.

. Bubeck, . Sébastien, . Munos, . Rémi, and G. Stoltz, Pure Exploration in Multi-armed Bandits Problems. Algorithmic Learning Theory, pp.23-37, 2009.

. Bubeck, . Sébastien, . Munos, . Rémi, . Stoltz et al., X-armed bandits, Journal of Machine Learning Research, vol.12, pp.1587-1627, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00450235

. Bubeck, . Sébastien, . Stoltz, . Gilles, and Y. Yuan, Lipschitz Bandits without the Lipschitz Constant, Algorithmic Learning Theory, pp.144-158, 2011.
DOI : 10.1007/978-3-642-24412-4_14

URL : https://hal.archives-ouvertes.fr/hal-00595692

A. Bull, Convergence rates of efficient global optimization algorithms, The Journal of Machine Learning Research, vol.12, pp.2879-2904, 2011.

P. Coquelin and R. Munos, Bandit Algorithms for Tree Search, Uncertainty in Artificial Intelligence, pp.67-74, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00150207

. Gelly, . Sylvain, . Kocsis, . Levente, . Schoenauer et al., The grand challenge of computer Go, Communications of the ACM, vol.55, issue.3, pp.106-113, 2012.
DOI : 10.1145/2093548.2093574

URL : https://hal.archives-ouvertes.fr/hal-00695370

E. Hansen and W. Walster, Global Optimization Using Interval Analysis: Revised and Expanded, Pure and Applied Mathematics Series. Marcel Dekker, 2004.

J. Hren and R. Munos, Optimistic Planning of Deterministic Systems, European Workshop on Reinforcement Learning, pp.151-164, 2008.
DOI : 10.1007/978-3-540-89722-4_12

URL : https://hal.archives-ouvertes.fr/hal-00830182

D. Jones, . Perttunen, . Cary, and B. Stuckman, Lipschitzian optimization without the Lipschitz constant, Journal of Optimization Theory and Applications, vol.20, issue.1, pp.157-181, 1993.
DOI : 10.1007/BF00941892

R. Kearfott and . Baker, Rigorous Global Search: Continuous Problems. Nonconvex Optimization and Its Applications, 1996.
DOI : 10.1007/978-1-4757-2495-0

R. Kleinberg, A. Slivkins, and E. Upfal, Multi-armed bandit problems in metric spaces, Proceedings of the 40th ACM symposium on Theory Of Computing, pp.681-690, 2008.

L. Kocsis and C. Szepesvári, Bandit Based Monte-Carlo Planning, Proceedings of the 15th European Conference on Machine Learning, pp.282-293, 2006.
DOI : 10.1007/11871842_29

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

R. Munos, Optimistic Optimization of Deterministic Functions without the Knowledge of its Smoothness, Advances in Neural Information Processing Systems, pp.783-791, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00830143

A. Neumaier, Interval Methods for Systems of Equations. Encyclopedia of Mathematics and its Applications, 2008.

M. Osborne, Bayesian Gaussian processes for sequential prediction, optimisation and quadrature, 2010.

J. Pintér, Global Optimization in Action: Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications. Nonconvex Optimization and Its Applications, 1995.
DOI : 10.1007/978-1-4757-2502-5

A. Slivkins, Multi-armed bandits on implicit metric spaces, Advances in Neural Information Processing Systems 24, pp.1602-1610, 2011.

. Srinivas, . Niranjan, . Krause, . Andreas, . Kakade et al., Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Proceedings of International Conference on Machine Learning, pp.1015-1022, 2010.

R. Strongin and Y. Sergeyev, Global Optimization with Non-Convex Constraints: Sequential and Parallel Algorithms. Nonconvex Optimization and Its Applications, 2000.
DOI : 10.1007/978-1-4615-4677-1