J. Abernethy, E. Hazan, and A. Rakhlin, Competing in the dark: An efficient algorithm for bandit linear optimization, Proceedings of the 21st Annual Conference on Learning Theory, pp.27-46, 2008.

A. Antoniadis, E. Paparoditis, and T. Sapatinas, A functional wavelet?kernel approach for time series prediction, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.42, issue.5, pp.837-857, 2006.
DOI : 10.1073/pnas.42.1.43

A. Antoniadis, X. Brossat, J. Cugliari, and J. Poggi, CLUSTERING FUNCTIONAL DATA USING WAVELETS, Proceedings of the Nineteenth International Conference on Computational Statistics (COMPSTAT), 2010.
DOI : 10.1142/S0219691313500033

URL : https://hal.archives-ouvertes.fr/inria-00559115

A. Antoniadis, X. Brossat, J. Cugliari, and J. Poggi, Prévision d'un processus à valeurs fonctionnelles en présence de non stationnarités. Application à la consommation d'électricité, pp.52-78, 2012.

A. Antoniadis, X. Brossat, J. Cugliari, and J. Poggi, CLUSTERING FUNCTIONAL DATA USING WAVELETS, International Journal of Wavelets, Multiresolution and Information Processing, vol.11, issue.01, 2013.
DOI : 10.1142/S0219691313500033

URL : https://hal.archives-ouvertes.fr/inria-00559115

J. Audibert and S. Bubeck, Regret bounds and minimax policies under partial monitoring, Journal of Machine Learning Research, vol.11, pp.2635-2686, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00654356

J. Audibert, S. Bubeck, and G. Lugosi, Regret in Online Combinatorial Optimization, Mathematics of Operations Research, vol.39, issue.1, pp.31-45, 2014.
DOI : 10.1287/moor.2013.0598

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

N. Auer, Y. Cesa-bianchi, R. Freund, and . Schapire, The Nonstochastic Multiarmed Bandit Problem, SIAM Journal on Computing, vol.32, issue.1, pp.48-77, 2002.
DOI : 10.1137/S0097539701398375

K. Azoury and M. Warmuth, Relative loss bounds for on-line density estimation with the exponential family of distributions, Machine Learning, pp.211-246, 2001.

M. Beckman, C. Mcguire, and C. Winsten, Studies in the Economics of Transportation, 1956.

V. Belmega and P. Mertikopoulos, Transmit without regrets: Online optimization in mimoâ??ofdm cognitive radio systems, IEEE Journal on Selected Areas in Communications, vol.32, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01073500

M. Benaïm, Dynamics of stochastic approximation algorithms, 1999.
DOI : 10.1007/978-1-4757-1947-5

M. Benaïm and M. Faure, Consistency of Vanishingly Smooth Fictitious Play, Mathematics of Operations Research, vol.38, issue.3, pp.437-450, 2013.
DOI : 10.1287/moor.1120.0568

M. Benaïm, J. Hofbauer, and S. Sorin, Stochastic Approximations and Differential Inclusions, SIAM Journal on Control and Optimization, vol.44, issue.1, pp.328-348, 2005.
DOI : 10.1137/S0363012904439301

M. Benaïm, J. Hofbauer, and S. Sorin, Stochastic Approximations and Differential Inclusions, SIAM Journal on Control and Optimization, vol.44, issue.1, pp.673-695, 2006.
DOI : 10.1137/S0363012904439301

A. Blum and Y. Mansour, From External to Internal Regret, Journal of Machine Learning Research, vol.8, pp.1307-1324, 2007.
DOI : 10.1007/11503415_42

S. Boucheron, P. Lugosi, and . Massart, Concentration Inequalities: A Nonasymptotic Theory of Independence, 2013.
DOI : 10.1093/acprof:oso/9780199535255.001.0001

URL : https://hal.archives-ouvertes.fr/hal-00794821

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

S. Bubeck, R. Munos, G. Stoltz, and C. Szepesvari, X-armed bandits, Journal of Machine Learning Research, vol.12, pp.1655-1695, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00450235

S. Bubeck, V. Perchet, and P. Rigollet, Bounded regret in stochastic multi-armed bandits, Proceedings of the 26th Annual Conference on Learning Theory (COLT), pp.122-134, 2013.

G. Cesa-bianchi and . Lugosi, Prediction, Learning, and Games, 2006.
DOI : 10.1017/CBO9780511546921

N. Cesa-bianchi, Y. Mansour, and G. Stoltz, Improved second-order bounds for prediction with expert advice, Machine Learning, pp.321-352, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00019799

C. Chiang, C. Yang, M. Lee, C. Mahdavi, R. Lu et al., Online optimization with gradual variations, Proceedings of the 25th Annual Conference on Learning Theory (COLT), pp.6-7, 2012.

H. Cho, Y. Goude, X. Brossat, and Q. Yao, Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach, Journal of the American Statistical Association, vol.73, issue.501, pp.7-21, 2013.
DOI : 10.1198/016214504000001745

H. Cho, Y. Goude, X. Brossat, and Q. Yao, Modeling and forecasting daily electricity load using curve linear regression, Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension, 2014.

P. Coucheney, S. Durand, B. Gaujal, and C. Touati, General Revision Protocols in Best Response Algorithms for Potential Games, Netwok Games, Control and OPtimization (NetGCoop), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01085077

P. Coucheney, B. Gaujal, and P. Mertikopoulos, Penalty-Regulated Dynamics and Robust Learning Procedures in Games, Mathematics of Operations Research, vol.40, issue.3, 2014.
DOI : 10.1287/moor.2014.0687

URL : https://hal.archives-ouvertes.fr/hal-01235243

T. Dani, S. Hayes, and . Kakade, The price of bandit information for online optimization, Advances in Neural Information Processing Systems 22, pp.345-352, 2008.

M. Devaine, P. Gaillard, Y. Goude, and G. Stoltz, Forecasting electricity consumption by aggregating specialized experts, Machine Learning, pp.231-260, 2013.
DOI : 10.1007/s10994-012-5314-7

URL : https://hal.archives-ouvertes.fr/hal-00484940

D. P. Foster, R. V. Vohra-fudenberg, and D. M. Kreps, Regret in the On-Line Decision Problem, Games and Economic Behavior, vol.29, issue.1-2, pp.7-35320, 1993.
DOI : 10.1006/game.1999.0740

D. Fudenberg and D. Levine, Consistency and cautious fictitious play, Journal of Economic Dynamics and Control, vol.19, issue.5-7, pp.1065-1089, 1995.
DOI : 10.1016/0165-1889(94)00819-4

P. Gaillard and Y. Goude, Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts, 2014.
DOI : 10.1007/978-3-319-18732-7_6

P. Gaillard, G. Stoltz, and T. Van-erven, A second-order bound with excess losses, Proceedings of COLT, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00943665

R. G. Gallager, A Minimum Delay Routing Algorithm Using Distributed Computation, IEEE Transactions on Communications, vol.25, issue.1, pp.73-85, 1977.
DOI : 10.1109/TCOM.1977.1093711

. Gerchinovitz, Sparsity regret bounds for individual sequences in online linear regression, Journal of Machine Learning Research, vol.14, pp.729-769, 2013.
URL : https://hal.archives-ouvertes.fr/inria-00552267

J. Hannan, Approximation to Bayes risk in repeated play, Contributions to the Theory of Games, pp.97-139, 1957.

S. Hart and A. Mas, A Simple Adaptive Procedure Leading to Correlated Equilibrium, Econometrica, vol.68, issue.5, pp.1127-1150, 2000.
DOI : 10.1111/1468-0262.00153

S. Hart and A. Mas, Uncoupled Dynamics Do Not Lead to Nash Equilibrium, American Economic Review, vol.93, issue.5, pp.1830-1836, 2003.
DOI : 10.1257/000282803322655581

S. Hart and A. Mas, Stochastic uncoupled dynamics and Nash equilibrium, Games and Economic Behavior, vol.57, issue.2, pp.286-303, 2006.
DOI : 10.1016/j.geb.2005.09.007

E. Hazan, The convex optimization approach to regret minimization. Optimization for machine learning, p.287, 2012.

E. Hazan and S. Kale, Extracting certainty from uncertainty: regret bounded by??variation in??costs, Machine Learning, pp.165-188, 2010.
DOI : 10.1007/s10994-010-5175-x

N. Kleinberg, Nearly tight bounds for the continuum-armed bandit problem, Advances in Neural Information Processing Systems 18, 2004.

J. Kwon and P. Mertikopoulos, A continuous-time approach to online optimization. arXiv preprint, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01382299

T. L. Lai and H. Robbins, Asymptotically optimal allocation of treatments in sequential experiments, Design of Experiments: Ranking and Selection, pp.127-142, 1984.

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

N. Littlestone and M. Warmuth, The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994.
DOI : 10.1006/inco.1994.1009

G. Lugosi, S. Mannor, and G. Stoltz, Strategies for Prediction Under Imperfect Monitoring, Mathematics of Operations Research, vol.33, issue.3, pp.513-528, 2008.
DOI : 10.1287/moor.1080.0312

URL : https://hal.archives-ouvertes.fr/hal-00124679

D. Monderer and L. Shapley, Potential games. Games and economic behavior, pp.124-143, 1996.

A. Orda, R. Rom, and N. Shimkin, Competitive routing in multiuser communication networks, IEEE/ACM Transactions on Networking, vol.1, issue.5, pp.510-521, 1993.
DOI : 10.1109/90.251910

V. Perchet, No-regret with partial monitoring: Calibration-based optimal algorithms, J. Mach. Learn. Res, vol.12, pp.1893-1921, 2011.

V. Perchet, Exponential Weight Approachability, Applications to Calibration and Regret Minimization, Dynamic Games And Applications, 2014.
DOI : 10.1007/s13235-014-0119-x

V. Perchet and P. Rigollet, The multi-armed bandit problem with covariates, The Annals of Statistics, vol.41, issue.2, pp.693-721
DOI : 10.1214/13-AOS1101

A. Pierrot and Y. Goude, Short-term electricity load forecasting with generalized additive models, Proceedings of ISAP power, pp.593-600, 2011.

A. Pierrot, N. Laluque, and Y. Goude, Short-term electricity load forecasting with generalized additive models, Proceedings of the Third International Conference on Computational and Financial Econometrics, 2009.

R. Rosenthal, A class of games possessing pure-strategy Nash equilibria, International Journal of Game Theory, vol.2, issue.1, pp.65-67, 1973.
DOI : 10.1007/BF01737559

T. Roughgarden, Selfish Routing and the Price of Anarchy, 2005.
DOI : 10.1017/CBO9781316779309.012

A. Rustichini, Minimizing Regret: The General Case, Games and Economic Behavior, vol.29, issue.1-2, pp.224-243, 1999.
DOI : 10.1006/game.1998.0690

. Sorin, Exponential weight algorithm in continuous time, Mathematical Programming, pp.513-528, 2009.
DOI : 10.1007/s10107-007-0111-y

P. Taylor and L. Jonker, Evolutionary stable strategies and game dynamics, Mathematical Biosciences, vol.40, issue.1-2, pp.145-156, 1978.
DOI : 10.1016/0025-5564(78)90077-9

V. G. Vovk, AGGREGATING STRATEGIES, Proceedings of the Third Workshop on Computational Learning Theory, pp.371-386, 1990.
DOI : 10.1016/B978-1-55860-146-8.50032-1

J. G. Wardrop, Some theoretical aspects of road traffic research. part ii, Proc. of the Institute of Civil Engineers, pp.325-378, 1954.

O. Wintenberger, Optimal learning with bernstein online aggregation. Extended version available at arXiv:1404, pp.1356-2014
URL : https://hal.archives-ouvertes.fr/hal-00973918

M. Woodroofe, A One-Armed Bandit Problem with a Concomitant Variable, Journal of the American Statistical Association, vol.38, issue.368, pp.799-806, 1979.
DOI : 10.1080/01621459.1979.10481033