| Ranking and empirical minimization of U-statistics S Clémençon, G Lugosi, N Vayatis Annals of Statistics 36 (2), 844-874, 2008 | 323 | 2008 |
| On the Bayes-risk consistency of regularized boosting methods G Lugosi, N Vayatis The Annals of statistics 32 (1), 30-55, 2004 | 258 | 2004 |
| Selective review of offline change point detection methods C Truong, L Oudre, N Vayatis Signal Processing 167, 107299, 2020 | 241 | 2020 |
| Estimation of simultaneously sparse and low rank matrices E Richard, PA Savalle, N Vayatis Proceedings of ICML'12, 2012 | 162 | 2012 |
| 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 | 139 | 2003 |
| 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 | 135 | 2013 |
| Ranking the best instances S Clémençon, N Vayatis The Journal of Machine Learning Research 8, 2671-2699, 2007 | 122 | 2007 |
| 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 | 114 | 2005 |
| Ranking and scoring using empirical risk minimization S Clemençon, G Lugosi, N Vayatis International Conference on Computational Learning Theory, 1-15, 2005 | 100 | 2005 |
| Tree-based ranking methods S Clémençon, N Vayatis IEEE Transactions on Information Theory 55 (9), 4316-4336, 2009 | 88 | 2009 |
| Gaussian process optimization with mutual information E Contal, V Perchet, N Vayatis International Conference on Machine Learning, 253-261, 2014 | 73 | 2014 |
| Quantitative analysis of dynamic fault trees based on the structure function G Merle, JM Roussel, JJ Lesage Quality and Reliability Engineering International 30 (1), 143-156, 2014 | 64* | 2014 |
| Ranking forests S Clémençon, M Depecker, N Vayatis Journal of Machine Learning Research 14 (Jan), 39-73, 2013 | 57 | 2013 |
| Global optimization of lipschitz functions C Malherbe, N Vayatis International Conference on Machine Learning, 2314-2323, 2017 | 45 | 2017 |
| Empirical performance maximization for linear rank statistics S Clemençon, N Vayatis Empirical performance maximization for linear rank statistics, 2009 | 45 | 2009 |
| Gap-free bounds for stochastic multi-armed bandit A Juditsky, AV Nazin, AB Tsybakov, N Vayatis IFAC Proceedings Volumes 41 (2), 11560-11563, 2008 | 44 | 2008 |
| 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 | 38 | 2014 |
| Link prediction in graphs with autoregressive features E Richard, S Gaïffas, N Vayatis The Journal of Machine Learning Research 15 (1), 565-593, 2014 | 37 | 2014 |
| Adaptive partitioning schemes for bipartite ranking S Clémençon, M Depecker, N Vayatis Machine Learning 83 (1), 31-69, 2011 | 36 | 2011 |
| 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 | 34 | 2016 |
Laurent OudreProfessor, Centre Borelli, ENS Paris-SaclayVerified email at ens-paris-saclay.fr
