S. R. Kulkarni, S. K. Mitter, and J. N. Tsitsiklis, Active learning using arbitrary binary valued queries, Machine Learning, vol.11, issue.1, pp.23-35, 1993.
DOI : 10.1023/A:1022627018023

D. Cohn, L. Atlas, and R. Ladner, Improving generalization with active learning, Machine Learning, vol.27, issue.4, pp.201-221, 1994.
DOI : 10.1007/BF00993277

G. Schohn and D. Cohn, Less is more: Active learning with support vector machines, Int. Conf. on Machine Learning, vol.282, pp.285-286, 2000.

S. Dasgupta, Analysis of a greedy active learning strategy, NIPS 17, pp.337-344, 2005.

R. Castro, R. Willett, and R. Nowak, Faster rates in regression via active learning, NIPS 18, pp.179-186, 2006.

S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu, Batch mode active learning and its application to medical image classification, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.417-424, 2006.
DOI : 10.1145/1143844.1143897

S. Hanneke, A bound on the label complexity of agnostic active learning, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.353-360, 2007.
DOI : 10.1145/1273496.1273541

L. Kocsis and C. Szepesvari, Bandit Based Monte-Carlo Planning, Eur. Conf. on Machine Learning, pp.282-293, 2006.
DOI : 10.1007/11871842_29

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.1296

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

P. Ruján, Playing Billiards in Version Space, Neural Computation, vol.1989, issue.1, pp.99-122, 1997.
DOI : 10.1209/0295-5075/21/8/013

R. Herbrich, T. Graepel, and C. Campbell, Bayes point machines, Journal of Machine Learning Research, vol.1, pp.245-279, 2001.

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

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

G. Chaslot, M. Winands, J. Uiterwijk, H. Van-den-herik, and B. Bouzy, Progressive strategies for Monte-Carlo tree search, Proc. of the 10th Joint Conf. on Information Sciences, pp.655-661, 2007.

Y. Wang, J. Y. Audibert, and R. Munos, Algorithms for infinitely many-armed bandits, NIPS 21, pp.1729-1736, 2009.

H. S. Seung, M. Opper, and H. Sompolinsky, Query by committee, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.287-294, 1992.
DOI : 10.1145/130385.130417

Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, Selective sampling using the query by committee algorithm, Mach. Learn, vol.28, pp.2-3, 1997.

D. Cohn, Z. Ghahramani, and M. Jordan, Active Learning with Statistical Models, Journal of Artificial Intelligence Research, vol.4, pp.129-145, 1996.

N. Roy and A. Mccallum, Toward optimal active learning through sampling estimation of error reduction, Int. Conf. on Machine Learning, pp.441-448, 2001.

M. Lindenbaum, S. Markovitch, and D. Rusakov, Selective Sampling for Nearest Neighbor Classifiers, Machine Learning, vol.54, issue.2, pp.125-152, 2004.
DOI : 10.1023/B:MACH.0000011805.60520.fe

S. Dasgupta, A. T. Kalai, and C. Monteleoni, Analysis of Perceptron-Based Active Learning, COLT'05, pp.249-263, 2005.
DOI : 10.1007/11503415_17

N. Cesa-bianchi, A. Conconi, and C. Gentile, Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling, COLT'03, pp.373-386, 2003.
DOI : 10.1007/978-3-540-45167-9_28

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.6405

F. Balcan, M. Broder, A. Zhang, and T. , Margin Based Active Learning, COLT'07, 2007.
DOI : 10.1007/978-3-540-72927-3_5

G. Xiao, F. Southey, R. C. Holte, and D. Wilkinson, Software testing by active learning for commercial games, pp.5-609, 2005.

M. Vidyasagar, A Theory of Learning and Generalization, with Applications to Neural Networks and Control Systems, 1997.

T. Hegedüs, Generalized teaching dimensions and the query complexity of learning, Proceedings of the eighth annual conference on Computational learning theory , COLT '95, pp.108-117, 1995.
DOI : 10.1145/225298.225311

S. Dasgupta, Coarse sample complexity bounds for active learning, NIPS 18, pp.235-242, 2006.

D. Haussler, M. Kearns, and R. E. Schapire, Bounds on the sample complexity of bayesian learning using information theory and the VC dimension, Mach. Learn, vol.14, issue.1, pp.83-113, 1994.

D. J. Mackay, Bayesian Interpolation, Neural Computation, vol.49, issue.3, pp.415-447, 1992.
DOI : 10.1093/comjnl/11.2.185

R. Sutton and A. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

P. Rolet, M. Sebag, and O. Teytaud, Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm, 2009.
DOI : 10.1007/978-3-642-04174-7_20

URL : https://hal.archives-ouvertes.fr/inria-00433866

R. Bellman, Dynamic Programming, 1957.

P. Auer, Using confidence bounds for exploitation-exploration trade-offs, The Journal of Machine Learning Research, vol.3, pp.397-422, 2003.

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

F. Comets, S. Popov, G. M. Schütz, and M. Vachkovskaia, Billiards in a General Domain with Random Reflections, Archive for Rational Mechanics and Analysis, vol.52, issue.1, pp.497-537, 2009.
DOI : 10.1007/s00205-008-0120-x

URL : https://hal.archives-ouvertes.fr/hal-00127179

L. Kocsis and C. Szepesvari, Bandit Based Monte-Carlo Planning, Eur. Conf. on Machine Learning, pp.282-293, 2006.
DOI : 10.1007/11871842_29

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.1296

Y. Freund and R. Schapire, Large margin classification using the perceptron algorithm, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, 1998.
DOI : 10.1145/279943.279985