Richard Nock
Richard Nock
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Adresse e-mail validée de uniagro.fr
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Statistical region merging
R Nock, F Nielsen
IEEE Transactions on pattern analysis and machine intelligence 26 (11), 1452 …, 2004
9902004
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
5502019
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
4542017
On weighting clustering
R Nock, F Nielsen
IEEE transactions on pattern analysis and machine intelligence 28 (8), 1223-1235, 2006
3192006
Sided and symmetrized Bregman centroids
F Nielsen, R Nock
IEEE transactions on Information Theory 55 (6), 2882-2904, 2009
1762009
A hybrid filter/wrapper approach of feature selection using information theory
M Sebban, R Nock
Pattern recognition 35 (4), 835-846, 2002
1642002
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
1282017
Bregman voronoi diagrams
JD Boissonnat, F Nielsen, R Nock
Discrete & Computational Geometry 44 (2), 281-307, 2010
1212010
Bregman Voronoi diagrams: Properties, algorithms and applications
F Nielsen, JD Boissonnat, R Nock
arXiv preprint arXiv:0709.2196, 2007
1182007
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
962006
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
952013
Bregman divergences and surrogates for learning
R Nock, F Nielsen
IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (11), 2048 …, 2008
782008
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
772016
Fitting the smallest enclosing Bregman ball
R Nock, F Nielsen
European Conference on Machine Learning, 649-656, 2005
622005
Semi-supervised statistical region refinement for color image segmentation
R Nock, F Nielsen
Pattern Recognition 38 (6), 835-846, 2005
602005
Entropies and cross-entropies of exponential families
F Nielsen, R Nock
2010 IEEE International Conference on Image Processing, 3621-3624, 2010
592010
On the smallest enclosing information disk
F Nielsen, R Nock
Information Processing Letters 105 (3), 93-97, 2008
592008
A Real generalization of discrete AdaBoost
R Nock, F Nielsen
Artificial Intelligence 171 (1), 25-41, 2007
572007
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
562011
Approximating smallest enclosing balls with applications to machine learning
F Nielsen, R Nock
International Journal of Computational Geometry & Applications 19 (05), 389-414, 2009
552009
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