| Selective review of offline change point detection methods C Truong, L Oudre, N Vayatis Signal Processing 167, 107299, 2020 | 1856 | 2020 |
| Ranking and Empirical Minimization of U-statistics S Clémençon, G Lugosi, N Vayatis | 474 | 2008 |
| A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open‐access code F Quijoux, A Nicolaï, I Chairi, I Bargiotas, D Ricard, A Yelnik, L Oudre, ... Physiological reports 9 (22), e15067, 2021 | 329 | 2021 |
| On the Bayes-risk consistency of regularized boosting methods G Lugosi, N Vayatis The Annals of statistics 32 (1), 30-55, 2004 | 305 | 2004 |
| 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 | 273 | 2013 |
| Estimation of simultaneously sparse and low rank matrices E Richard, PA Savalle, N Vayatis Proceedings of ICML'12, 2012 | 252 | 2012 |
| Global optimization of Lipschitz functions C Malherbe, N Vayatis International conference on machine learning, 2314-2323, 2017 | 173 | 2017 |
| 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 | 164 | 2003 |
| Gaussian process optimization with mutual information E Contal, V Perchet, N Vayatis International Conference on Machine Learning, 253-261, 2014 | 135 | 2014 |
| Ranking the best instances S Clémençon, N Vayatis The Journal of Machine Learning Research 8, 2671-2699, 2007 | 134 | 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 | 116 | 2005 |
| 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 | 112 | 2016 |
| Ranking and scoring using empirical risk minimization S Clemençon, G Lugosi, N Vayatis International conference on computational learning theory, 1-15, 2005 | 105 | 2005 |
| Tree-based ranking methods S Clémençon, N Vayatis IEEE Transactions on Information Theory 55 (9), 4316-4336, 2009 | 104 | 2009 |
| Adapt: Awesome domain adaptation python toolbox A de Mathelin, M Atiq, G Richard, A de la Concha, M Yachouti, ... arXiv preprint arXiv:2107.03049, 2021 | 85 | 2021 |
| Ranking forests S Clémençon, M Depecker, N Vayatis The Journal of Machine Learning Research 14 (1), 39-73, 2013 | 75 | 2013 |
| Nonparametric markovian learning of triggering kernels for mutually exciting and mutually inhibiting multivariate hawkes processes R Lemonnier, N Vayatis Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 68 | 2014 |
| Adversarial weighting for domain adaptation in regression A De Mathelin, G Richard, F Deheeger, M Mougeot, N Vayatis 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 65 | 2021 |
| Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall I Bargiotas, D Wang, J Mantilla, F Quijoux, A Moreau, C Vidal, R Barrois, ... Journal of neurology 270 (2), 618-631, 2023 | 63 | 2023 |
| 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 | 60 | 2014 |
Laurent OudreFull Professor, Centre Borelli, ENS Paris-SaclayVerified email at ens-paris-saclay.fr
