Sébastien Gerchinovitz
Sébastien Gerchinovitz
Research scientist, IRT Saint Exupéry, Toulouse
Verified email at - Homepage
Cited by
Cited by
Sparsity regret bounds for individual sequences in online linear regression
S Gerchinovitz
Journal of Machine Learning Research 14 (Mar), 729-769, 2013
Refined lower bounds for adversarial bandits
S Gerchinovitz, T Lattimore
Advances in Neural Information Processing Systems 29, 1198-1206, 2016
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 (COLT 2017), 465-481, 2017
A chaining algorithm for online nonparametric regression
P Gaillard, S Gerchinovitz
Conference on Learning Theory (COLT 2015), 764-796, 2015
Prediction of individual sequences and prediction in the statistical framework: some links around sparse regression and aggregation techniques
S Gerchinovitz
Université Paris-Sud XI, 2011
Fano’s inequality for random variables
S Gerchinovitz, P Ménard, G Stoltz
Statistical Science 35 (2), 178-201, 2020
Numerical influence of ReLU’(0) on backpropagation
D Bertoin, J Bolte, S Gerchinovitz, E Pauwels
Advances in Neural Information Processing Systems 34, 468-479, 2021
A multiple-play bandit algorithm applied to recommender systems
J Louëdec, M Chevalier, J Mothe, A Garivier, S Gerchinovitz
The Twenty-Eighth International FLAIRS Conference, 67-72, 2015
Optimization of a SSP's Header Bidding Strategy using Thompson Sampling
G Jauvion, N Grislain, P Dkengne Sielenou, A Garivier, S Gerchinovitz
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
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
Object Detection With Probabilistic Guarantees: A Conformal Prediction Approach
F de Grancey, JL Adam, L Alecu, S Gerchinovitz, F Mamalet, D Vigouroux
Fifth International Workshop on Artificial Intelligence Safety Engineering …, 2022
The loss landscape of deep linear neural networks: a second-order analysis
EM Achour, F Malgouyres, S Gerchinovitz
Optimal functional supervised classification with separation condition
S Gadat, S Gerchinovitz, C Marteau
Bernoulli 26 (3), 1797-1831, 2020
Adaptive and optimal online linear regression on ℓ1-balls
S Gerchinovitz, JY Yu
Theoretical Computer Science 519, 4-28, 2014
Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimization
C Bouttier, T Cesari, M Ducoffe, S Gerchinovitz
arXiv preprint arXiv:2002.02390, 2020
Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates
F Bachoc, T Cesari, S Gerchinovitz
Advances in Neural Information Processing Systems 34, 2021
A High Probability Safety Guarantee for Shifted Neural Network Surrogates.
M Ducoffe, S Gerchinovitz, JS Gupta
SafeAI@ AAAI, 74-82, 2020
Adaptive simulated annealing with homogenization for aircraft trajectory optimization
C Bouttier, O Babando, S Gadat, S Gerchinovitz, S Laporte, F Nicol
Operations Research Proceedings 2015, 569-574, 2017
Can we reconcile safety objectives with machine learning performances?
L Alecu, H Bonnin, T Fel, L Gardes, S Gerchinovitz, L Ponsolle, F Mamalet, ...
ERTS 2022, 2022
A further look at sequential aggregation rules for ozone ensemble forecasting
S Gerchinovitz, V Mallet, G Stoltz
Rapport technique, INRIA Paris-Rocquencourt et École normale supérieure, Paris, 2008
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