J. Audibert, R. Munos, and C. Szepesvári, Tuning Bandit Algorithms in Stochastic Environments, ALT, vol.25, issue.2-3, pp.150-165, 2007.
DOI : 10.1093/biomet/25.3-4.285

URL : https://hal.archives-ouvertes.fr/inria-00203487

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

G. Chaslot, D. Jong, S. Saito, J. Uiterwijk, and J. W. , Monte-Carlo tree search in production management problems, Proc. of the 18th BeNeLux Conference on AI, pp.91-98, 2006.

V. Cicirello and S. Smith, The max k-armed bandit: A new model for exploration applied to search heuristic selection, Artificial Intelligence, 2005.

R. Coulom, Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, Proc. of the 5th International Conference on Computers and Games, 2006.
DOI : 10.1007/978-3-540-75538-8_7

URL : https://hal.archives-ouvertes.fr/inria-00116992

F. Franchetti, Y. Voronenko, and M. Püschel, FFT program generation for shared memory: SMP and multicore . Supercomputing (SC), 2006.

F. Franchetti, Y. Voronenko, and M. Püschel, A Rewriting System for the Vectorization of Signal Transforms, pp.363-377, 2006.
DOI : 10.1007/978-3-540-71351-7_28

M. Frigo and S. G. Johnson, The Design and Implementation of FFTW3, Proc. of the IEEE, pp.216-231, 2005.
DOI : 10.1109/JPROC.2004.840301

S. Gelly and D. Silver, Combining online and offline knowledge in UCT, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.273-280, 2007.
DOI : 10.1145/1273496.1273531

URL : https://hal.archives-ouvertes.fr/inria-00164003

L. Kocsis and C. Szepesvi, Bandit Based Monte-Carlo Planning, ECML'06 LNCS 4212, pp.282-293, 2006.
DOI : 10.1007/11871842_29

T. Lai and H. Robbins, Asymptotically efficient adaptive allocation rules, Advances in Applied Mathematics, vol.6, issue.1, pp.4-22, 1985.
DOI : 10.1016/0196-8858(85)90002-8

D. Mirkovi´cmirkovi´c, R. Mahasoom, and L. Johnsson, An adaptive software library for fast Fourier transforms, Proc. of the 14th Supercomputing, pp.215-224, 2000.

B. Singer and M. Veloso, Learning to construct fast signal processing implementations, Journal of Machine Learning Research, vol.3, pp.887-919, 2001.

M. J. Streeter and S. F. Smith, A simple distribution-free approach to the max k-armed bandit problem. Principles and Practice of Constraint Programming, pp.560-574, 2006.

Y. Voronenko, Library generation for linear transforms . Doctoral dissertation, 2008.

Y. Voronenko, F. De-mesmay, and M. Püschel, Computer Generation of General Size Linear Transform Libraries, 2009 International Symposium on Code Generation and Optimization, 2009.
DOI : 10.1109/CGO.2009.33

R. Vuduc, J. W. Demmel, and K. A. Yelick, OSKI: A library of automatically tuned sparse matrix kernels, Journal of Physics: Conference Series, vol.16, p.16, 2005.
DOI : 10.1088/1742-6596/16/1/071

Y. Wang and S. Gelly, Modifications of UCT and sequence-like simulations for Monte-Carlo Go, 2007 IEEE Symposium on Computational Intelligence and Games, pp.175-182, 2007.
DOI : 10.1109/CIG.2007.368095

R. C. Whaley and A. Petitet, Minimizing development and maintenance costs in supporting persistently optimized BLAS. Software: Practice and Experience, pp.101-121, 2005.