Nicolas Vayatis
Nicolas Vayatis
Professeur des universités, ENS Paris-Saclay
Verified email at ens-paris-saclay.fr - Homepage
Title
Cited by
Cited by
Year
Selective review of offline change point detection methods
C Truong, L Oudre, N Vayatis
Signal Processing 167, 107299, 2020
373*2020
Ranking and empirical minimization of U-statistics
S Clémençon, G Lugosi, N Vayatis
The Annals of Statistics 36 (2), 844-874, 2008
3452008
On the Bayes-risk consistency of regularized boosting methods
G Lugosi, N Vayatis
The Annals of statistics 32 (1), 30-55, 2004
2722004
Estimation of simultaneously sparse and low rank matrices
E Richard, PA Savalle, N Vayatis
Proceedings of ICML'12, 2012
1732012
Parallel Gaussian process optimization with upper confidence bound and pure exploration
E Contal, D Buffoni, A Robicquet, N Vayatis
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013
1562013
On the rate of convergence of regularized boosting classifiers
G Blanchard, G Lugosi, N Vayatis
Journal of Machine Learning Research 4 (Oct), 861-894, 2003
1482003
Ranking the best instances
S Clémençon, N Vayatis
The Journal of Machine Learning Research 8, 2671-2699, 2007
1262007
Recursive aggregation of estimators by the mirror descent algorithm with averaging
AB Juditsky, AV Nazin, AB Tsybakov, N Vayatis
Problems of Information Transmission 41 (4), 368-384, 2005
1162005
Ranking and scoring using empirical risk minimization
S Clemençon, G Lugosi, N Vayatis
International Conference on Computational Learning Theory, 1-15, 2005
1032005
Tree-based ranking methods
S Clémençon, N Vayatis
IEEE Transactions on Information Theory 55 (9), 4316-4336, 2009
942009
Gaussian process optimization with mutual information
E Contal, V Perchet, N Vayatis
International Conference on Machine Learning, 253-261, 2014
752014
Ranking forests
S Clémençon, M Depecker, N Vayatis
Journal of Machine Learning Research 14 (Jan), 39-73, 2013
622013
Global optimization of lipschitz functions
C Malherbe, N Vayatis
International Conference on Machine Learning, 2314-2323, 2017
612017
Empirical performance maximization for linear rank statistics
S Clémençon, N Vayatis
Empirical performance maximization for linear rank statistics, 2009
482009
Gap-free bounds for stochastic multi-armed bandit
A Juditsky, AV Nazin, AB Tsybakov, N Vayatis
IFAC Proceedings Volumes 41 (2), 11560-11563, 2008
462008
Prediction and optimization of wave energy converter arrays using a machine learning approach
D Sarkar, E Contal, N Vayatis, F Dias
Renewable Energy 97, 504-517, 2016
422016
Can small islands protect nearby coasts from tsunamis? An active experimental design approach
TS Stefanakis, E Contal, N Vayatis, F Dias, CE Synolakis
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2014
402014
Link prediction in graphs with autoregressive features
E Richard, S Gaïffas, N Vayatis
The Journal of Machine Learning Research 15 (1), 565-593, 2014
382014
Adaptive partitioning schemes for bipartite ranking
S Clémençon, M Depecker, N Vayatis
Machine Learning 83 (1), 31-69, 2011
382011
Overlaying classifiers: a practical approach to optimal scoring
S Clémençon, N Vayatis
Constructive Approximation 32 (3), 619-648, 2010
372010
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