| Selective review of offline change point detection methods C Truong, L Oudre, N Vayatis Signal Processing 167, 107299, 2020 | 659 | 2020 |
| Ranking and Empirical Minimization of U-statistics S Clémençon, G Lugosi, N Vayatis | 377 | 2008 |
| On the Bayes-risk consistency of regularized boosting methods G Lugosi, N Vayatis The Annals of statistics 32 (1), 30-55, 2004 | 279 | 2004 |
| Estimation of simultaneously sparse and low rank matrices E Richard, PA Savalle, N Vayatis Proceedings of ICML'12, 2012 | 206 | 2012 |
| Parallel Gaussian process optimization with upper confidence bound and pure exploration E Contal, D Buffoni, A Robicquet, N Vayatis Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 196 | 2013 |
| 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 | 152 | 2003 |
| Ranking the best instances S Clémençon, N Vayatis The Journal of Machine Learning Research 8, 2671-2699, 2007 | 128 | 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 | 118 | 2005 |
| Ranking and scoring using empirical risk minimization S Clemençon, G Lugosi, N Vayatis Learning Theory: 18th Annual Conference on Learning Theory, COLT 2005 …, 2005 | 102 | 2005 |
| Tree-based ranking methods S Clémençon, N Vayatis IEEE Transactions on Information Theory 55 (9), 4316-4336, 2009 | 96 | 2009 |
| Gaussian process optimization with mutual information E Contal, V Perchet, N Vayatis International Conference on Machine Learning, 253-261, 2014 | 94 | 2014 |
| Global optimization of Lipschitz functions C Malherbe, N Vayatis International Conference on Machine Learning, 2314-2323, 2017 | 91 | 2017 |
| Ranking forests S Clémençon, M Depecker, N Vayatis Journal of Machine Learning Research 14, 39-73, 2013 | 70 | 2013 |
| 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 | 60 | 2016 |
| Gap-free bounds for stochastic multi-armed bandit A Juditsky, AV Nazin, AB Tsybakov, N Vayatis IFAC Proceedings Volumes 41 (2), 11560-11563, 2008 | 50 | 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 | 47 | 2014 |
| Empirical performance maximization for linear rank statistics S Clémençcon, N Vayatis Advances in neural information processing systems 21, 2008 | 46 | 2008 |
| Overlaying classifiers: a practical approach to optimal scoring S Clémençon, N Vayatis Constructive Approximation 32, 619-648, 2010 | 45 | 2010 |
| ruptures: change point detection in Python C Truong, L Oudre, N Vayatis arXiv preprint arXiv:1801.00826, 2018 | 44 | 2018 |
| Nonparametric markovian learning of triggering kernels for mutually exciting and mutually inhibiting multivariate hawkes processes R Lemonnier, N Vayatis Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 44 | 2014 |
Laurent OudreProfessor, Centre Borelli, ENS Paris-SaclayVerified email at ens-paris-saclay.fr
