F. Bach, Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression, The Journal of Machine Learning Research, vol.15, issue.1, pp.595-627, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00804431

R. H. Byrd, S. L. Hansen, J. Nocedal, and Y. Singer, A stochastic quasi-newton method for largescale optimization, SIAM Journal on Optimization, vol.26, issue.2, pp.1008-1031, 2016.

S. Clémençon, P. Bertail, E. Chautru, and G. Papa, Survey schemes for stochastic gradient descent with applications to m-estimation, 2015.

M. Duflo, Translated from the 1990 French original by Stephen S. Wilson and revised by the author, Applications of Mathematics, vol.34, 1997.

S. Gadat and F. Panloup, Optimal non-asymptotic bound of the ruppert-polyak averaging without strong convexity, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01623986

A. Godichon-baggioni, Lp and almost sure rates of convergence of averaged stochastic gradient algorithms with applications to online robust estimation, 2016.

A. Godichon-baggioni, Online estimation of the asymptotic variance for averaged stochastic gradient algorithms, 2017.

D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied logistic regression, vol.398, 2013.

O. Komori, S. Eguchi, S. Ikeda, H. Okamura, M. Ichinokawa et al., An asymmetric logistic regression model for ecological data, Methods in Ecology and Evolution, vol.7, issue.2, pp.249-260, 2016.

A. Lucchi, B. Mcwilliams, and T. Hofmann, A variance reduced stochastic newton method, 2015.

J. Merlo, P. Wagner, N. Ghith, and G. Leckie, An original stepwise multilevel logistic regression analysis of discriminatory accuracy: the case of neighbourhoods and health, PLoS One, vol.11, issue.4, p.153778, 2016.

A. Mokhtari and A. Ribeiro, Res: Regularized stochastic bfgs algorithm, IEEE Transactions on Signal Processing, vol.62, issue.23, pp.6089-6104, 2014.

M. Pelletier, Asymptotic almost sure efficiency of averaged stochastic algorithms, SIAM J. Control Optim, vol.39, issue.1, pp.49-72, 2000.

B. Polyak and A. Juditsky, Acceleration of stochastic approximation, SIAM J. Control and Optimization, vol.30, pp.838-855, 1992.

H. Robbins and S. Monro, A stochastic approximation method. The annals of mathematical statistics, pp.400-407, 1951.

D. Ruppert, Efficient estimations from a slowly convergent robbins-monro process, 1988.

H. R. Varian, Big data: New tricks for econometrics, Journal of Economic Perspectives, vol.28, issue.2, pp.3-28, 2014.
DOI : 10.1257/jep.28.2.3
URL : https://www.aeaweb.org/articles/pdf/doi/10.1257/jep.28.2.3