| Statistical region merging R Nock, F Nielsen IEEE Transactions on pattern analysis and machine intelligence 26 (11), 1452 …, 2004 | 980 | 2004 |
| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... arXiv preprint arXiv:1912.04977, 2019 | 471 | 2019 |
| Making deep neural networks robust to label noise: A loss correction approach G Patrini, A Rozza, A Krishna Menon, R Nock, L Qu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 417 | 2017 |
| On weighting clustering R Nock, F Nielsen IEEE transactions on pattern analysis and machine intelligence 28 (8), 1223-1235, 2006 | 317 | 2006 |
| Sided and symmetrized Bregman centroids F Nielsen, R Nock IEEE transactions on Information Theory 55 (6), 2882-2904, 2009 | 174 | 2009 |
| A hybrid filter/wrapper approach of feature selection using information theory M Sebban, R Nock Pattern recognition 35 (4), 835-846, 2002 | 161 | 2002 |
| Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption S Hardy, W Henecka, H Ivey-Law, R Nock, G Patrini, G Smith, B Thorne arXiv preprint arXiv:1711.10677, 2017 | 120 | 2017 |
| Bregman voronoi diagrams JD Boissonnat, F Nielsen, R Nock Discrete & Computational Geometry 44 (2), 281-307, 2010 | 120 | 2010 |
| Bregman Voronoi diagrams: Properties, algorithms and applications F Nielsen, JD Boissonnat, R Nock arXiv preprint arXiv:0709.2196, 2007 | 118 | 2007 |
| Learning and evaluation in the presence of class hierarchies: Application to text categorization S Kiritchenko, S Matwin, R Nock, AF Famili Conference of the Canadian Society for Computational Studies of Intelligence …, 2006 | 96 | 2006 |
| On the chi square and higher-order chi distances for approximating f-divergences F Nielsen, R Nock IEEE Signal Processing Letters 21 (1), 10-13, 2013 | 93 | 2013 |
| Bregman divergences and surrogates for learning R Nock, F Nielsen IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (11), 2048 …, 2008 | 78 | 2008 |
| Loss factorization, weakly supervised learning and label noise robustness G Patrini, F Nielsen, R Nock, M Carioni International conference on machine learning, 708-717, 2016 | 75 | 2016 |
| Fitting the smallest enclosing Bregman ball R Nock, F Nielsen European Conference on Machine Learning, 649-656, 2005 | 62 | 2005 |
| Semi-supervised statistical region refinement for color image segmentation R Nock, F Nielsen Pattern Recognition 38 (6), 835-846, 2005 | 60 | 2005 |
| On the smallest enclosing information disk F Nielsen, R Nock Information Processing Letters 105 (3), 93-97, 2008 | 59 | 2008 |
| A Real generalization of discrete AdaBoost R Nock, F Nielsen Artificial Intelligence 171 (1), 25-41, 2007 | 57 | 2007 |
| Entropies and cross-entropies of exponential families F Nielsen, R Nock 2010 IEEE International Conference on Image Processing, 3621-3624, 2010 | 55 | 2010 |
| Approximating smallest enclosing balls with applications to machine learning F Nielsen, R Nock International Journal of Computational Geometry & Applications 19 (05), 389-414, 2009 | 55 | 2009 |
| A closed-form expression for the Sharma–Mittal entropy of exponential families F Nielsen, R Nock Journal of Physics A: Mathematical and Theoretical 45 (3), 032003, 2011 | 53 | 2011 |
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