Y. Abbasi-yadkori, D. Pal, and C. Szepesvari, Improved Algorithms for Linear Stochastic Bandits, Advances in Neural Information Processing Systems 24, pp.2312-2320, 2011.

P. Auer, Using confidence bounds for exploitation-exploration trade-offs, Journal of Machine Learning Research, vol.3, pp.397-422, 2002.

P. Auer, N. Cesa-bianchi, Y. Freund, and R. E. Schapire, The Nonstochastic Multiarmed Bandit Problem, SIAM Journal on Computing, vol.32, issue.1, pp.48-77, 2003.
DOI : 10.1137/S0097539701398375

A. Beygelzimer, J. Langford, L. Li, L. Reyzin, and R. E. Schapire, Contextual Bandit Algorithms with Supervised Learning Guarantees, Machine Learning, p.14, 2010.

S. Bubeck and N. Cesa-bianchi, Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Machine Learning, pp.1-122, 2012.
DOI : 10.1561/2200000024

Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, and L. Cazzanti, Similarity-based Classification: Concepts and Algorithms, Journal of Machine Learning Research, vol.10, issue.206, pp.747-776, 2009.

L. Chu, L. Li, L. Reyzin, and R. E. Schapire, Contextual Bandits with Linear Payoff Functions, Proceedings of the 14th International Conference on Articial Intelligence and Statis- tics, 2011.

V. Dani, T. P. Hayes, and S. M. Kakade, Stochastic Linear Optimization under Bandit Feedback, The 21st Annual Conference on Learning Theory, pp.355-366, 2008.

M. Dudik, D. Hsu, S. Kale, N. Karampatziakis, J. Langford et al., Efficient Optimal Learning for Contextual Bandits, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, 2011.

S. Grünewälder, J. Audibert, M. Opper, and J. Shawe-taylor, Regret Bounds for Gaussian Process Bandit Problems, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010.

B. Haasdonk and E. Pekalska, Classification with Kernel Mahalanobis Distance Classifiers Advances in Data Analysis, Data Handling and Business Intelligence, pp.351-361, 2010.

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

A. Krause and C. S. Ong, Contextual Gaussian Process Bandit Optimization, Proceedings of Neural Information Processing Systems (NIPS), 2011.

T. L. 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

J. Langford and T. Zhang, The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information, Advances in Neural Information Processing Systems, pp.817-824, 2008.

L. Li, W. Chu, J. Langford, and R. E. Schapire, A contextual-bandit approach to personalized news article recommendation, Proceedings of the 19th international conference on World wide web, WWW '10, p.10, 2010.
DOI : 10.1145/1772690.1772758

T. Lu, D. Pál, and M. Pál, Contextual Multi- Armed Bandits, Proceedings of the 13th international conference on Artificial Intelligence and Statistics, pp.485-492, 2010.

P. Rusmevichientong and J. N. Tsitsiklis, Linearly Parameterized Bandits, Mathematics of Operations Research, vol.35, issue.2, pp.395-411, 2010.
DOI : 10.1287/moor.1100.0446
URL : http://arxiv.org/abs/0812.3465

Y. Seldin, P. Auer, F. Laviolette, J. S. Shawe-taylor, and R. Ortner, PAC-Bayesian Analysis of Contextual Bandits, Neural Information Processing Systems (NIPS), pp.1683-1691, 2011.

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

A. Slivkins, Contextual Bandits with Similarity Information, Proceedings of the 24th annual Conference On Learning Theory, pp.1-27, 2009.

N. Srinivas, A. Krause, S. Kakade, and M. Seeger, Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Proceedings of International Conference on Machine Learning, pp.1015-1022, 2010.

R. Steinberger, B. Pouliquen, and E. Van-der-goot, An Introduction to the {Europe Media Monitor} Family of Applications, Information Access in a Multilingual World-Proceedings of the SIGIR 2009 Workshop (SIGIR-CLIR'2009), pp.1-8, 2009.

F. Zhang, The Schur complement and its applications, 2005.
DOI : 10.1007/b105056