E. Atanassov and I. Dimov, A new optimal Monte Carlo method for calculating integrals of smooth functions, Monte Carlo Methods and Applications, vol.5, issue.2, pp.149-167, 1999.
DOI : 10.1515/mcma.1999.5.2.149

E. Atanassov and I. Dimov, What Monte Carlo models can do and cannot do efficiently?, Applied Mathematical Modelling, vol.32, issue.8, pp.1477-1500, 2008.
DOI : 10.1016/j.apm.2007.04.010

URL : http://doi.org/10.1016/j.apm.2007.04.010

K. Baggerly, D. Cox, and R. Picard, Exponential convergence of adaptive importance sampling for Markov chains, Journal of Applied Probability, vol.50, issue.02, pp.342-358, 2000.
DOI : 10.1017/S0001867800025192

T. B. Zineb and E. Gobet, Preliminary control variates to improve empirical regression methods, pp.331-354, 2013.

C. Bender and J. Steiner, Least-squares Monte Carlo for BSDEs, Numerical Methods in Finance, Series: Springer Proceedings in Mathematics, pp.257-289, 2012.

T. Booth, Exponential convergence for Monte Carlo particle transport?, Trans. Amer. Nucl. Soc, vol.50, pp.267-268, 1985.

B. Bouchard and X. Warin, Monte-Carlo Valuation of American Options: Facts and New Algorithms to Improve Existing Methods, Numerical Methods in Finance, Series: Springer Proceedings in Mathematics, pp.215-255, 2012.
DOI : 10.1007/978-3-642-25746-9_7

H. J. Bungartz and M. , Sparse grids, Acta Numerica, vol.13, pp.147-269, 2004.
DOI : 10.1017/S0962492904000182

C. Canuto, M. Hussaini, A. Quarteroni, and T. Zang, Spectral Methods, 2006.
DOI : 10.1002/0470091355.ecm003m

URL : https://hal.archives-ouvertes.fr/hal-01296839

D. Egloff, Monte Carlo algorithms for optimal stopping and statistical learning, The Annals of Applied Probability, vol.15, issue.2, pp.1396-1432, 2005.
DOI : 10.1214/105051605000000043

D. Funaro, Polynomial approximation of differential equations, volume 8 of Lecture Notes in Physics. New Series m: Monographs, 1992.

E. Gobet and C. Labart, Solving BSDE with Adaptive Control Variate, SIAM Journal on Numerical Analysis, vol.48, issue.1, pp.257-277, 2010.
DOI : 10.1137/090755060

URL : https://hal.archives-ouvertes.fr/hal-00373350

E. Gobet, J. P. Lemor, and X. Warin, A regression-based Monte Carlo method to solve backward stochastic differential equations, The Annals of Applied Probability, vol.15, issue.3, pp.2172-2202, 2005.
DOI : 10.1214/105051605000000412

E. Gobet and S. Maire, Sequential Control Variates for Functionals of Markov Processes, SIAM Journal on Numerical Analysis, vol.43, issue.3, pp.1256-1275, 2005.
DOI : 10.1137/040609124

URL : https://hal.archives-ouvertes.fr/hal-01479838

E. Gobet and P. Turkedjiev, Linear regression MDP scheme for discrete backward stochastic differential equations under general conditions, Mathematics of Computation, vol.85, issue.299, 2013.
DOI : 10.1090/mcom/3013

URL : https://hal.archives-ouvertes.fr/hal-00642685

O. Le-maître and O. Knio, Spectral Methods for Uncertainty Quantification . With Applications to Computational Fluid Dynamics, Scientific Computation, 2010.

J. P. Lemor, E. Gobet, and X. Warin, Rate of convergence of an empirical regression method for solving generalized backward stochastic differential equations, Bernoulli, vol.12, issue.5, pp.889-916, 2006.
DOI : 10.3150/bj/1161614951

URL : https://hal.archives-ouvertes.fr/hal-00394976

F. Longstaff and E. Schwartz, Valuing American Options by Simulation: A Simple Least-Squares Approach, Review of Financial Studies, vol.14, issue.1, pp.113-147, 2001.
DOI : 10.1093/rfs/14.1.113

S. Maire, An iterative computation of approximations on Korobov-like spaces, Journal of Computational and Applied Mathematics, vol.157, issue.2, pp.261-281, 2003.
DOI : 10.1016/S0377-0427(03)00410-2

URL : https://hal.archives-ouvertes.fr/hal-01479853

S. Maire, Reducing variance using iterated control variates, Journal of Statistical Computation and Simulation, vol.73, issue.1, pp.1-29, 2003.
DOI : 10.1080/00949650215726

URL : https://hal.archives-ouvertes.fr/hal-01479850