A. Agarwal, O. Dekel, and L. Xiao, Optimal algorithms for online convex optimization with multipoint bandit feedback, Proc. COLT, 2010.

A. Agarwal, D. Foster, D. Hsu, S. Kakade, and A. Rakhlin, Stochastic Convex Optimization with Bandit Feedback, SIAM Journal on Optimization, vol.23, issue.1, pp.188-212, 2013.
DOI : 10.1137/110850827

F. Bach, Self-concordant analysis for logistic regression, Electronic Journal of Statistics, vol.4, issue.0, pp.384-414, 2010.
DOI : 10.1214/09-EJS521

URL : https://hal.archives-ouvertes.fr/hal-00426227

F. Bach and E. Moulines, Non-asymptotic analysis of stochastic approximation algorithms for machine learning, Adv. NIPS, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00608041

F. Bach and E. Moulines, Non-strongly-convex smooth stochastic approximation with convergence rate o(1/n), Adv. NIPS, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00831977

S. Bubeck, Convex optimization: Algorithms and complexity. Foundations and Trends, Machine Learning, pp.231-357, 2015.
DOI : 10.1561/2200000050

URL : http://arxiv.org/abs/1405.4980

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

H. Chen, Lower Rate of Convergence for Locating a Maximum of a Function, The Annals of Statistics, vol.16, issue.3, pp.1330-1334, 1988.
DOI : 10.1214/aos/1176350965

J. Dippon, Accelerated randomized stochastic optimization, The Annals of Statistics, vol.31, issue.4, pp.1260-1281, 2003.
DOI : 10.1214/aos/1059655913

J. C. Duchi, M. I. Jordan, M. J. Wainwright, and A. Wibisono, Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations, IEEE Transactions on Information Theory, vol.61, issue.5, 2013.
DOI : 10.1109/TIT.2015.2409256

V. Fabian, Stochastic Approximation of Minima with Improved Asymptotic Speed, The Annals of Mathematical Statistics, vol.38, issue.1, pp.191-200, 1967.
DOI : 10.1214/aoms/1177699070

A. D. Flaxman, A. T. Kalai, and H. B. Mcmahan, Online convex optimization in the bandit setting: gradient descent without a gradient, Proc. Symposium on Discrete algorithms (SODA). Society for Industrial and Applied Mathematics, 2005.

E. Hazan and K. Levy, Bandit convex optimization: Towards tight bounds, Adv. NIPS, 2014.

E. Hazan, T. Koren, and K. Levy, Logistic regression: Tight bounds for stochastic and online optimization, Proc. Conference On Learning Theory (COLT), 2014.

C. Hu, W. Pan, and J. T. Kwok, Accelerated gradient methods for stochastic optimization and online learning, Advances in Neural Information Processing Systems, 2009.

S. M. Kakade, O. Shamir, K. Sridharan, and A. Tewari, Learning exponential families in highdimensions: Strong convexity and sparsity, pp.54-56, 2009.

H. Kushner and G. G. Yin, Stochastic approximation and Recursive Algorithms and Applications, 2003.

G. Lan, An optimal method for stochastic composite optimization, Mathematical Programming, pp.365-397, 2012.
DOI : 10.1007/s10107-010-0434-y

G. Lan, A. Nemirovski, and A. Shapiro, Validation analysis of mirror descent stochastic approximation method, Mathematical Programming, vol.24, issue.2, pp.425-458, 2012.
DOI : 10.1007/s10107-011-0442-6

T. Liang, H. Narayanan, and S. Sakhalin, On zeroth-order stochastic convex optimization via random walks, 2014.

A. S. Nemirovski and D. B. Yudin, Problem complexity and method efficiency in optimization, 1983.

A. Nemirovski, Interior point polynomial time methods in convex programming, Lecture Notes, 2004.

Y. Nesterov, Introductory Lectures on Convex Optimization, of Applied Optimization, 2004.
DOI : 10.1007/978-1-4419-8853-9

Y. Nesterov, Random Gradient-Free Minimization of Convex Functions, Foundations of Computational Mathematics, vol.66, issue.2, 2011.
DOI : 10.1007/s10208-015-9296-2

B. T. Polyak and A. B. Tsybakov, Optimal order of accuracy of search algorithms in stochastic optimization, Problemy Peredachi Informatsii, vol.26, issue.2, pp.45-53, 1990.