![Bertrand Gauthier](https://profiles-cardiff.imgix.net/attachments/176?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Bertrand Gauthier
Darlithydd
- GauthierB@caerdydd.ac.uk
- +44 29208 75544
- Abacws, Ystafell 2.07, Ffordd Senghennydd, Cathays, Caerdydd, CF24 4AG
Trosolwyg
Research Group
Statistics.
Research Interests
Kernel-based Models, Machine Learning, Design of Experiments, Computational Mathematics, Big Data.
Cyhoeddiad
2024
- Gauthier, B. 2024. Kernel embedding of measures and low-rank approximation of integral operators. Positivity 28, article number: 29. (10.1007/s11117-024-01041-8)
2023
- Hutchings, M. and Gauthier, B. 2023. Local optimisation of Nyström samples through stochastic gradient descent. Presented at: The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Siena, Italy, September 18 – 22, 2022. Vol. 13810. Springer pp. 123-140., (10.1007/978-3-031-25599-1_10)
2018
- Gauthier, B. and Suykens, J. A. K. 2018. Optimal quadrature-sparsification for integral operator approximation. SIAM Journal on Scientific Computing 40(5), pp. A3636-A3674. (10.1137/17M1123614)
2017
- Gauthier, B. and Pronzato, L. 2017. Convex relaxation for IMSE optimal design in random-field models. Computational Statistics and Data Analysis 113, pp. 375-394. (10.1016/j.csda.2016.10.018)
2016
- Gauthier, B. and Pronzato, L. 2016. Approximation of IMSE-optimal designs via quadrature rules and spectral decomposition. Communications in Statistics - Simulation and Computation 45(5), pp. 1600-1612. (10.1080/03610918.2014.972518)
- Gauthier, B. and Pronzato, L. 2016. Optimal design for prediction in random field models via covariance kernel expansions. In: Kunert, J., Muller, C. H. and Atkinson, A. C. eds. mODa 11 - Advances in Model-Oriented Design and Analysis: Proceedings of the 11th International Workshop in Model-Oriented Design and Analysis held in Hamminkeln, Germany, June 12-17, 2016. Springer, pp. 103-111., (10.1007/978-3-319-31266-8_13)
2014
- Gauthier, B. and Pronzato, L. 2014. Spectral approximation of the IMSE criterion for optimal designs in kernel-based interpolation models. SIAM/ASA Journal on Uncertainty Quantification 2(1), pp. 805-825. (10.1137/130928534)
2012
- Gauthier, B. and Bay, X. 2012. Spectral approach for kernel-based interpolation. Annales de la faculté des sciences de Toulouse Mathématiques 21(3), pp. 439-479. (10.5802/afst.1341)
Adrannau llyfrau
- Gauthier, B. and Pronzato, L. 2016. Optimal design for prediction in random field models via covariance kernel expansions. In: Kunert, J., Muller, C. H. and Atkinson, A. C. eds. mODa 11 - Advances in Model-Oriented Design and Analysis: Proceedings of the 11th International Workshop in Model-Oriented Design and Analysis held in Hamminkeln, Germany, June 12-17, 2016. Springer, pp. 103-111., (10.1007/978-3-319-31266-8_13)
Cynadleddau
- Hutchings, M. and Gauthier, B. 2023. Local optimisation of Nyström samples through stochastic gradient descent. Presented at: The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Siena, Italy, September 18 – 22, 2022. Vol. 13810. Springer pp. 123-140., (10.1007/978-3-031-25599-1_10)
Erthyglau
- Gauthier, B. 2024. Kernel embedding of measures and low-rank approximation of integral operators. Positivity 28, article number: 29. (10.1007/s11117-024-01041-8)
- Gauthier, B. and Suykens, J. A. K. 2018. Optimal quadrature-sparsification for integral operator approximation. SIAM Journal on Scientific Computing 40(5), pp. A3636-A3674. (10.1137/17M1123614)
- Gauthier, B. and Pronzato, L. 2017. Convex relaxation for IMSE optimal design in random-field models. Computational Statistics and Data Analysis 113, pp. 375-394. (10.1016/j.csda.2016.10.018)
- Gauthier, B. and Pronzato, L. 2016. Approximation of IMSE-optimal designs via quadrature rules and spectral decomposition. Communications in Statistics - Simulation and Computation 45(5), pp. 1600-1612. (10.1080/03610918.2014.972518)
- Gauthier, B. and Pronzato, L. 2014. Spectral approximation of the IMSE criterion for optimal designs in kernel-based interpolation models. SIAM/ASA Journal on Uncertainty Quantification 2(1), pp. 805-825. (10.1137/130928534)
- Gauthier, B. and Bay, X. 2012. Spectral approach for kernel-based interpolation. Annales de la faculté des sciences de Toulouse Mathématiques 21(3), pp. 439-479. (10.5802/afst.1341)
Ymchwil
My research mainly focuses on kernel-based learning methods, and on kernel-related numerical methods in general. I am particularly interested in the theoretical and computational challenges inherent to the application of kernel-based strategies to large scale problems. My topics of interest also include design of experiments, random-field models and machine learning.
Addysgu
Rwy'n Gymrawd o'r Academi Addysg Uwch (ers 2019).
Cyrsiau a addysgir ar hyn o bryd (2020/21):
- Dadansoddiad Data Aml-amrywiad (Blwyddyn 3, MA3506)
- Sylfeini Ystadegau a Gwyddor Data (MSc, MAT022)
Goruchwylio myfyrwyr y prosiect: Bob blwyddyn academaidd, rwy'n cynnig ac yn goruchwylio detholiad o brosiectau ar bynciau amrywiol. Os ydych yn astudio Mathemateg ym Mhrifysgol Caerdydd ar hyn o bryd ac yn ystyried gwneud prosiect ar bwnc sy'n gysylltiedig â Mathemateg Gwyddor Data, mae croeso i chi gysylltu â mi.
Bywgraffiad
Previous affiliations:
Ph.D student and teaching assistant at École des Mines de Saint-Étienne (France) from October 2007 to January 2011.
ATER (temporary research and teaching assistant) at University of Saint-Étienne (France), Mathematics Department, Institut Camille Jordan, from February 2011 to August 2012.
Postdoctoral researcher at CNRS - University of Nice-Sophia Antipolis (France), Laboratoire I3S, from September 2012 to August 2014; and CNRS contract agent on secondment to EDF-Lab Chatou (France), from September to December 2014.
Postdoctoral researcher at KU Leuven (Belgium), ESAT-STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, from March 2015 to December 2016.
Meysydd goruchwyliaeth
Goruchwyliaeth gyfredol
Matt Hutchings
Myfyriwr ymchwil