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.

A. Azzalini and A. Genz, The R package mnormt: The multivariate normal and t distributions (version 1.5-1), 2014.

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.22, issue.3, pp.773-793, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00689580

S. M. Berman, An extension of Plackett's differential equation for the multivariate normal density, SIAM Journal on Algebraic Discrete Methods, vol.8, issue.2, pp.196-197, 1987.

E. Brochu, M. Cora, and N. De-freitas, A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, 2010.

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

C. Chevalier, Fast uncertainty reduction strategies relying on Gaussian process models, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00879082

C. Chevalier and D. Ginsbourger, Revised Selected Papers, chapter fast computation of the multipoint expected improvement with applications in batch selection, 7th International Conference, vol.7, pp.59-69, 2013.

D. Ginsbourger, V. Picheny, O. Roustant, C. Chevalier, S. Marmin et al., DiceOptim: Kriging-Based Optimization for Computer Experiments, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00495766

T. Desautels, A. Krause, and J. Burdick, Parallelizing exploration-exploitation tradeoffs with gaussian process bandit optimization, ICML, 2012.

P. I. Frazier, Parallel global optimization using an improved multi-points expected improvement criterion, INFORMS Optimization Society Conference, 2012.

P. I. Frazier, W. B. Powell, and S. Dayanik, A knowledge-gradient policy for sequential information collection, SIAM Journal on Control and Optimization, vol.47, issue.5, pp.2410-2439, 2008.

A. Genz, Numerical computation of multivariate normal probabilities, Journal of Computational and Graphical Statistics, vol.1, pp.141-149, 1992.

D. Ginsbourger and R. Le-riche, Towards gaussian process-based optimization with finite time horizon, mODa 9 Advances in Model-Oriented Design and Analysis, pp.89-96, 2010.
URL : https://hal.archives-ouvertes.fr/emse-00680794

D. Ginsbourger, R. Le-riche, and L. Carraro, Kriging is well-suited to parallelize optimization, Computational Intelligence in Expensive Optimization Problems, vol.2, pp.131-162, 2010.
URL : https://hal.archives-ouvertes.fr/emse-00436126

D. R. Jones, M. Schonlau, and J. William, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.

Q. Y. Kenny, W. Li, and A. Sudjianto, Algorithmic construction of optimal symmetric latin hypercube designs, Journal of statistical planning and inference, vol.90, issue.1, pp.145-159, 2000.

W. Mebane and J. Sekhon, Genetic optimization using derivatives: The rgenoud package for r, Journal of Statistical Software, vol.42, pp.1-26, 2011.

J. Mockus, V. Tiesis, and A. Zilinskas, The application of Bayesian methods for seeking the extremum, Towards Global Optimization, vol.2, pp.117-129, 1978.

C. R. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, 2006.

O. Roustant, D. Ginsbourger, Y. Deville, and . Dicekriging, DiceOptim: Two R packages for the analysis of computer experiments by Kriging-Based Metamodelling and Optimization, Journal of Statistical Software, vol.51, issue.1, pp.1-55, 2012.
URL : https://hal.archives-ouvertes.fr/emse-00741762

M. Schonlau, Computer Experiments and global optimization, 1997.

N. Srinivas, A. Krause, S. Kakade, and M. Seeger, Information-theoretic regret bounds for gaussian process optimization in the bandit setting, IEEE Transactions on Information Theory, vol.58, issue.5, pp.3250-3265, 2012.

E. Vazquez and J. Bect, Convergence properties of the expected improvement algorithm with fixed mean and covariance functions, Journal of Statistical Planning and inference, vol.140, issue.11, pp.3088-3095, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00217562

J. Villemonteix, E. Vazquez, and E. Walter, An informational approach to the global optimization of expensive-to-evaluate functions, Journal of Global Optimization, vol.44, issue.4, pp.509-534, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00354262