| Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting P Gaillard, Y Goude, R Nedellec International Journal of forecasting 32 (3), 1038-1050, 2016 | 263 | 2016 |
| A second-order bound with excess losses P Gaillard, G Stoltz, T Van Erven Conference on Learning Theory, 176-196, 2014 | 176 | 2014 |
| Forecasting electricity consumption by aggregating specialized experts: A review of the sequential aggregation of specialized experts, with an application to Slovakian and … M Devaine, P Gaillard, Y Goude, G Stoltz Machine Learning 90, 231-260, 2013 | 130 | 2013 |
| Mirror descent meets fixed share (and feels no regret) N Cesa-Bianchi, P Gaillard, G Lugosi, G Stoltz Advances in Neural Information Processing Systems 25, 2012 | 99 | 2012 |
| Forecasting electricity consumption by aggregating experts; how to design a good set of experts P Gaillard, Y Goude Modeling and stochastic learning for forecasting in high dimensions, 95-115, 2015 | 68 | 2015 |
| Algorithmic chaining and the role of partial feedback in online nonparametric learning N Cesa-Bianchi, P Gaillard, C Gentile, S Gerchinovitz Conference on Learning Theory, 465-481, 2017 | 48 | 2017 |
| A chaining algorithm for online nonparametric regression P Gaillard, S Gerchinovitz Conference on Learning Theory, 764-796, 2015 | 47 | 2015 |
| Tight nonparametric convergence rates for stochastic gradient descent under the noiseless linear model R Berthier, F Bach, P Gaillard Advances in Neural Information Processing Systems 33, 2576-2586, 2020 | 46 | 2020 |
| opera: Online prediction by expert aggregation P Gaillard, Y Goude URL: https://CRAN. R-project. org/package= opera. r package version 1, 2016 | 42* | 2016 |
| Continuized accelerations of deterministic and stochastic gradient descents, and of gossip algorithms M Even, R Berthier, F Bach, N Flammarion, H Hendrikx, P Gaillard, ... Advances in Neural Information Processing Systems 34, 28054-28066, 2021 | 37* | 2021 |
| Accelerated gossip in networks of given dimension using jacobi polynomial iterations R Berthier, F Bach, P Gaillard SIAM Journal on Mathematics of Data Science 2 (1), 24-47, 2020 | 37 | 2020 |
| A new look at shifting regret N Cesa-Bianchi, P Gaillard, G Lugosi, G Stoltz arXiv preprint arXiv:1202.3323, 2012 | 32 | 2012 |
| Efficient improper learning for online logistic regression R Jézéquel, P Gaillard, A Rudi Conference on Learning Theory, 2085-2108, 2020 | 27 | 2020 |
| Versatile dueling bandits: Best-of-both world analyses for learning from relative preferences A Saha, P Gaillard International Conference on Machine Learning, 19011-19026, 2022 | 26* | 2022 |
| Improved sleeping bandits with stochastic action sets and adversarial rewards A Saha, P Gaillard, M Valko International Conference on Machine Learning, 8357-8366, 2020 | 24 | 2020 |
| Uniform regret bounds over for the sequential linear regression problem with the square loss P Gaillard, S Gerchinovitz, M Huard, G Stoltz Algorithmic Learning Theory, 404-432, 2019 | 24 | 2019 |
| Efficient online learning with kernels for adversarial large scale problems R Jézéquel, P Gaillard, A Rudi Advances in Neural Information Processing Systems 32, 2019 | 17 | 2019 |
| Efficient kernelized ucb for contextual bandits H Zenati, A Bietti, E Diemert, J Mairal, M Martin, P Gaillard International Conference on Artificial Intelligence and Statistics, 5689-5720, 2022 | 16 | 2022 |
| Sparse accelerated exponential weights P Gaillard, O Wintenberger Artificial Intelligence and Statistics, 75-82, 2017 | 14 | 2017 |
| Target tracking for contextual bandits: Application to demand side management M Brégère, P Gaillard, Y Goude, G Stoltz International Conference on Machine Learning, 754-763, 2019 | 13 | 2019 |
Yannig GoudeEDF R&D, LMO Université Paris-SaclayVerified email at edf.fr
