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Pierre Gaillard
Pierre Gaillard
INRIA
Verified email at gaillard.me - Homepage
Title
Cited by
Cited by
Year
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
2562016
A second-order bound with excess losses
P Gaillard, G Stoltz, T Van Erven
Conference on Learning Theory, 176-196, 2014
1652014
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
1282013
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
942012
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
672015
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
452017
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
442020
A chaining algorithm for online nonparametric regression
P Gaillard, S Gerchinovitz
Conference on Learning Theory, 764-796, 2015
442015
opera: Online prediction by expert aggregation
P Gaillard, Y Goude
URL: https://CRAN. R-project. org/package= opera. r package version 1, 2016
38*2016
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
362020
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
34*2021
A new look at shifting regret
N Cesa-Bianchi, P Gaillard, G Lugosi, G Stoltz
CoRR, abs/1202.3323, 2012
312012
Efficient improper learning for online logistic regression
R Jézéquel, P Gaillard, A Rudi
Conference on Learning Theory, 2085-2108, 2020
262020
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
232019
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
20*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
202020
Efficient online learning with kernels for adversarial large scale problems
R Jézéquel, P Gaillard, A Rudi
Advances in Neural Information Processing Systems 32, 2019
172019
Sparse accelerated exponential weights
P Gaillard, O Wintenberger
Artificial Intelligence and Statistics, 75-82, 2017
142017
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
132019
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
122022
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