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CV Nicolas Couellan

Journal articles12 documents

  • Nicolas Couellan. The coupling effect of Lipschitz regularization in neural networks. SN Computer Science, Springer, 2021, 2 (2), pp.113. ⟨10.1007/s42979-021-00514-x⟩. ⟨hal-02090498⟩
  • Ambre Diet, Nicolas Couellan, Xavier Gendre, Julien Martin. A Chernov bound for robust tolerance design and application. International Journal of Advanced Manufacturing Technology, Springer Verlag, 2020, 111, pp.3571-3581. ⟨10.1007/s00170-020-06231-8⟩. ⟨hal-02442568⟩
  • Nicolas Couellan, Sophie Jan. Feature uncertainty bounds for explicit feature maps and large robust nonlinear SVM classifiers. Annals of Mathematics and Artificial Intelligence, Springer Verlag, 2020, 88 (1-3), pp.269-289. ⟨10.1007/s10472-019-09676-0⟩. ⟨hal-01718729⟩
  • Nicolas Couellan. A note on supervised classification and Nash-equilibrium problems. RAIRO - Operations Research, EDP Sciences, 2017, 51 (2), pp.329-341. ⟨10.1051/ro/2016024⟩. ⟨hal-01354857⟩
  • Nicolas Couellan, Wenjuan Wang. Uncertainty-safe large scale support vector machines. Computational Statistics and Data Analysis, Elsevier, 2017, 109, pp.215 - 230. ⟨10.1016/j.csda.2016.12.008⟩. ⟨hal-01921969⟩
  • Nicolas Couellan, Wenjuan Wang. Bi-level Stochastic Gradient for Large Scale Support Vector Machine. Neurocomputing, Elsevier, 2015, 153, pp.300-308. ⟨10.1016/j.neucom.2014.11.025⟩. ⟨hal-01354854⟩
  • Nicolas Couellan, Tom Jorquera, Jean-Pierre Georgé, Sophie Jan. Self Adaptive Support Vector Machine: A Multi-Agent Optimization Perspective. Expert Systems with Applications, Elsevier, 2015, 42 (9), pp.4284-4298. ⟨10.1016/j.eswa.2015.01.028⟩. ⟨hal-01387802⟩
  • Nicolas Couellan, Theodore B. Trafalis. On-line SVM learning via an incremental primal-dual technique. Optimization Methods and Software, Taylor & Francis, 2013, 28 (2), p. 256-275. ⟨10.1080/10556788.2011.633705⟩. ⟨hal-00965822⟩
  • Nicolas Couellan, Theodore B. Trafalis. An incremental primal-dual method for nonlinear programming with special structure. Optimization Letters, Springer Verlag, 2013, 7 (1), p. 51-62. ⟨10.1007/s11590-011-0393-0⟩. ⟨hal-00965828⟩
  • Nicolas Couellan, Sophie Jan. Incremental accelerated gradient methods for SVM classification : study of the constrained approach. Computational Management Science, Springer Verlag, 2013. ⟨hal-00955704⟩
  • Theodore Trafalis, Nicolas Couellan. An Incremental Nonlinear Primal-Dual Algorithm and Applications to Artificial Neural Networks Training. IFAC Proceedings Volumes, Elsevier, 1998, 31 (20), pp.1003 - 1009. ⟨10.1016/S1474-6670(17)41929-X⟩. ⟨hal-01922713⟩
  • Nicolas Couellan, Theodore B. Trafalis. Neural network training via an affine scaling quadratic optimization algorithm. Neural Networks, Elsevier, 1996, 9 (3), pp.475-481. ⟨hal-01354862⟩

Conference papers11 documents

  • Ambre Diet, Nicolas Couellan, Xavier Gendre, Julien Martin, Jean-Philippe Navarro. A statistical approach for tolerancing from design stage to measurements analysis. CIRP CAT 2020 16th CIRP Conference on Computer Aided Tolerancing, Jun 2020, Virtual event, United States. pp.33-38, ⟨10.1016/j.procir.2020.05.171⟩. ⟨hal-02976044⟩
  • Evgenii Munin, Antoine Blais, Nicolas Couellan. GNSS Multipath Detection using Embedded Deep CNN on Intel Neural Compute Stick. ION GNSS+ 2020, 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation, Sep 2020, Virtual event, United States. pp. 2018-2029., ⟨10.33012/2020.17654⟩. ⟨hal-02964023⟩
  • Nicolas Couellan. Probabilistic Robustness Estimates for Deep Neural Networks. ICML workshop on Uncertainty and Robustness in Deep Learning, International Conference on Machine Learning (ICML), Jul 2020, Virtual Conference, United States. ⟨hal-02572277v2⟩
  • Evgenii Munin, Antoine Blais, Nicolas Couellan. Convolutional Neural Network for Multipath Detection in GNSS Receivers. AIDA-AT 2020, 1st conference on Artificial Intelligence and Data Analytics in Air Transportation, Feb 2020, Singapore, Singapore. ⟨10.1109/AIDA-AT48540.2020.9049188⟩. ⟨hal-02359943⟩
  • Gabriel Jarry, Nicolas Couellan, Daniel Delahaye. On the Use of Generative Adversarial Networks for Aircraft Trajectory Generation and Atypical Approach Detection. EIWAC 2019:, 6th ENRI International Workshop on ATM/CNS, Oct 2019, Tokyo, Japan. ⟨hal-02267170⟩
  • Nicolas Couellan, Wenjuan Wang. Bi-level Stochastic Gradient for Large Scale Support Vector Machine. 12th EUROPT Workshop on Continuous Optimization, 2014, Perpignan, France. ⟨hal-01923085⟩
  • Nicolas Couellan, Theodore B. Trafalis. An Adaptive Weighted Kernel Technique for Online Training with Imbalanced Data. EURO 2012, 25th European Conference on Operational Research, 2012, Vinius, Lithuania. ⟨hal-01923087⟩
  • Nicolas Couellan, Theodore B. Trafalis. An Incremental Interior Point Method for On-line SVM Learning. AFG’11: 15th Austrian-French-German Conference on Optimization, 2011, Toulouse, France. ⟨hal-01923089⟩
  • Nicolas Couellan, T.B. Trafalis, S.C. Bertrand. Training of supervised neural networks via a nonlinear primal-dual interior-point method. International Conference on Neural Networks (ICNN'97), Jun 1997, Houston, United States. ⟨10.1109/ICNN.1997.614210⟩. ⟨hal-01922685⟩
  • Nicolas Couellan, Theodore B. Trafalis, P. I., Greg Stumpf, Andy White. Affine scaling neural network training algorithm for prediction of tornados. Intelligent Engineering Systems Through Artificial Neural Networks, 1997, New York, France. pp. 213-218. ⟨hal-01922705⟩
  • Nicolas Couellan, Theodore B. Trafalis. Neural Network training via a primal-dual interior point method for nonlinear programming. World Congress on Neural Networks: 1994 International Neural Network Society Annual Meeting, 1994, Hillsdale, NJ, United States. ⟨hal-01922751⟩

Book sections2 documents

  • Nicolas Couellan, Theodore B. Trafalis. Neural Network Training via Quadratic Programming. Barr R.S., Helgason R.V., Kennington J.L. Interfaces in Computer Science and Operations Research, 7, Springer, 1997. ⟨hal-01922634⟩
  • Nicolas Couellan, Theodore B. Trafalis, Tarek Tutunji. Interior Point Methods for Supervised Training of Artificial Neural Networks with Bounded Weights. Pardalos P.M., Hearn D.W., Hager W.W. Network Optimization, 450, Springer, Berlin, Heidelberg, 1997, Lecture Notes in Economics and Mathematical Systems, ⟨10.1007/978-3-642-59179-2_22⟩. ⟨hal-01922663⟩

Preprints, Working Papers, ...2 documents

  • Laurent Risser, Quentin Vincenot, Nicolas Couellan, Jean-Michel Loubes. Using Wasserstein-2 regularization to ensure fair decisions with Neural-Network classifiers. 2019. ⟨hal-02271117⟩
  • Nicolas Couellan, Wenjuan Wang. On the convergence of a stochastic approximation method for structured bi-level optimization. 2018. ⟨hal-01932372⟩

Reports1 document

  • Stergos Afantenos, Nicolas Couellan, Aurélien Garivier, Sébastien Gerchinovitz, Jean-Michel Loubes, et al.. Report on CIMI Thematic Trimester: Machine Learning. [Rapport de recherche] IRIT : Institut de Recherche en Informatique de Toulouse. 2016. ⟨hal-03155046⟩