Francois Bachoc
Professeur des universités
CNU : SECTION 26 - Mathematiques appliquees et applications des mathematiques
Laboratoire / équipe
Publications
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"citationFull_s":"François Bachoc. Report of the ANR JCJC project “GAussian Processes for computer experiments and machine learning: more guarantees and broader applications” (GAP), 2021-2026. Laboratoire Paul Painlevé - UMR 8524. 2026. <a target=\"_blank\" href=\"https://hal.science/hal-05705879v1\">⟨hal-05705879⟩</a>",
"title_s":["Report of the ANR JCJC project “GAussian Processes for computer experiments and machine learning: more guarantees and broader applications” (GAP), 2021-2026"],
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"citationRef_s":"<i>Statistics and Computing</i>, 2026, 36 (1), pp.42. <a target=\"_blank\" href=\"https://dx.doi.org/10.1007/s11222-025-10791-8\">⟨10.1007/s11222-025-10791-8⟩</a>",
"citationFull_s":"Mathis Deronzier, Andrés F. López-Lopera, François Bachoc, Olivier Roustant, Jérémy Rohmer. Block-Additive Gaussian Processes under Monotonicity Constraints. <i>Statistics and Computing</i>, 2026, 36 (1), pp.42. <a target=\"_blank\" href=\"https://dx.doi.org/10.1007/s11222-025-10791-8\">⟨10.1007/s11222-025-10791-8⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-05441153v1\">⟨hal-05441153⟩</a>",
"title_s":["Block-Additive Gaussian Processes under Monotonicity Constraints"],
"authFullName_s":["Mathis Deronzier","Andrés F. López-Lopera","François Bachoc","Olivier Roustant","Jérémy Rohmer"],
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"citationRef_s":"<i>Conference on Neural Information Processing Systems</i>, Dec 2025, San Diego (CA), United States",
"citationFull_s":"François Bachoc, Jérôme Bolte, Ryan Boustany, Jean-Michel Loubes. When majority rules, minority loses: bias amplification of gradient descent. <i>Conference on Neural Information Processing Systems</i>, Dec 2025, San Diego (CA), United States. <a target=\"_blank\" href=\"https://hal.science/hal-05072199v2\">⟨hal-05072199v2⟩</a>",
"title_s":["When majority rules, minority loses: bias amplification of gradient descent"],
"authFullName_s":["François Bachoc","Jérôme Bolte","Ryan Boustany","Jean-Michel Loubes"],
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"citationFull_s":"Florian Gossard, François Bachoc, Jean Baccou, Thibaut Le Gouic, Jacques Liandrat, et al.. Kriging measure-valued data with sparse observations: application to nuclear safety studies. 2025. <a target=\"_blank\" href=\"https://hal.science/hal-05326334v1\">⟨hal-05326334⟩</a>",
"title_s":["Kriging measure-valued data with sparse observations: application to nuclear safety studies"],
"authFullName_s":["Florian Gossard","François Bachoc","Jean Baccou","Thibaut Le Gouic","Jacques Liandrat","Tony Glantz"],
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"citationFull_s":"Elie Odin, François Bachoc, Agnès Lagnoux. Contraction rates and projection subspace estimation with Gaussian process priors in high dimension. 2025. <a target=\"_blank\" href=\"https://hal.science/hal-04490400v4\">⟨hal-04490400v4⟩</a>",
"title_s":["Contraction rates and projection subspace estimation with Gaussian process priors in high dimension"],
"authFullName_s":["Elie Odin","François Bachoc","Agnès Lagnoux"],
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"citationFull_s":"Laurence Grammont, François Bachoc, Andrés F. López-Lopera. Error Bounds for a Kernel-Based Constrained Optimal Smoothing Approximation. 2025. <a target=\"_blank\" href=\"https://hal.science/hal-04645086v2\">⟨hal-04645086v2⟩</a>",
"title_s":["Error Bounds for a Kernel-Based Constrained Optimal Smoothing Approximation"],
"authFullName_s":["Laurence Grammont","François Bachoc","Andrés F. López-Lopera"],
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"citationRef_s":"<i>ESAIM: Probability and Statistics</i>, 2025, 29, pp.204-242. <a target=\"_blank\" href=\"https://dx.doi.org/10.1051/ps/2025004\">⟨10.1051/ps/2025004⟩</a>",
"citationFull_s":"François Bachoc, Cathy Maugis, Pierre Neuvial. Selective inference after convex clustering with $\\ell_1$ penalization. <i>ESAIM: Probability and Statistics</i>, 2025, 29, pp.204-242. <a target=\"_blank\" href=\"https://dx.doi.org/10.1051/ps/2025004\">⟨10.1051/ps/2025004⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04200062v2\">⟨hal-04200062v2⟩</a>",
"title_s":["Selective inference after convex clustering with $\\ell_1$ penalization"],
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"citationFull_s":"Dominique Bontemps, François Bachoc, Pierre Neuvial. Inference post region selection. 2025. <a target=\"_blank\" href=\"https://hal.science/hal-05109459v1\">⟨hal-05109459⟩</a>",
"title_s":["Inference post region selection"],
"authFullName_s":["Dominique Bontemps","François Bachoc","Pierre Neuvial"],
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"citationFull_s":"Robert Koprinkov, François Bachoc, Nathalie Bartoli, Sylvain Dubreuil. Structured Gaussian Process Regression for Multiphysics Problems. <i>COUPLED PROBLEMS 2025 - International Conference on Coupled Problems in Science and Engineering</i>, May 2025, Villasimius, Italy. <a target=\"_blank\" href=\"https://hal.science/hal-05121407v1\">⟨hal-05121407⟩</a>",
"title_s":["Structured Gaussian Process Regression for Multiphysics Problems"],
"authFullName_s":["Robert Koprinkov","François Bachoc","Nathalie Bartoli","Sylvain Dubreuil"],
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"citationRef_s":"<i>Annals of Statistics</i>, 2025, 53 (4)",
"citationFull_s":"François Bachoc, Louis Béthune, Alberto González-Sanz, Jean-Michel Loubes. Improved learning theory for kernel distribution regression with two-stage sampling. <i>Annals of Statistics</i>, 2025, 53 (4). <a target=\"_blank\" href=\"https://hal.science/hal-04956911v1\">⟨hal-04956911⟩</a>",
"title_s":["Improved learning theory for kernel distribution regression with two-stage sampling"],
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"citationRef_s":"<i>Journal of Multivariate Analysis</i>, 2025, 205, pp.105380. <a target=\"_blank\" href=\"https://dx.doi.org/10.1016/j.jmva.2024.105380\">⟨10.1016/j.jmva.2024.105380⟩</a>",
"citationFull_s":"Lucas Reding, Andrés F. López-Lopera, François Bachoc. Covariance parameter estimation of Gaussian processes with approximated functional inputs. <i>Journal of Multivariate Analysis</i>, 2025, 205, pp.105380. <a target=\"_blank\" href=\"https://dx.doi.org/10.1016/j.jmva.2024.105380\">⟨10.1016/j.jmva.2024.105380⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04844122v1\">⟨hal-04844122⟩</a>",
"title_s":["Covariance parameter estimation of Gaussian processes with approximated functional inputs"],
"authFullName_s":["Lucas Reding","Andrés F. López-Lopera","François Bachoc"],
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"citationFull_s":"Mathis Deronzier, Andrés F. López-Lopera, François Bachoc, O Roustant, Jérémy Rohmer. Block-Additive Gaussian Processes under Monotonicity Constraints. 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04654255v1\">⟨hal-04654255⟩</a>",
"title_s":["Block-Additive Gaussian Processes under Monotonicity Constraints"],
"authFullName_s":["Mathis Deronzier","Andrés F. López-Lopera","François Bachoc","O Roustant","Jérémy Rohmer"],
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"citationFull_s":"François Bachoc, Tommaso R Cesari, Roberto Colomboni. A Contextual Online Learning Theory of Brokerage. 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04619452v1\">⟨hal-04619452⟩</a>",
"title_s":["A Contextual Online Learning Theory of Brokerage"],
"authFullName_s":["François Bachoc","Tommaso R Cesari","Roberto Colomboni"],
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"citationFull_s":"Adama Barry, François Bachoc, Sarah Bouquet, Miguel Munoz Munoz Zuniga, Clémentine Prieur. Design of experiments for computer code calibration. 2024. <a target=\"_blank\" href=\"https://inria.hal.science/hal-04615127v3\">⟨hal-04615127v3⟩</a>",
"title_s":["Design of experiments for computer code calibration"],
"authFullName_s":["Adama Barry","François Bachoc","Sarah Bouquet","Miguel Munoz Munoz Zuniga","Clémentine Prieur"],
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"citationFull_s":"Lucas Reding, Andrés F. López-Lopera, François Bachoc. Asymptotic analysis for covariance parameter estimation of Gaussian processes with functional inputs. 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04559892v2\">⟨hal-04559892v2⟩</a>",
"title_s":["Asymptotic analysis for covariance parameter estimation of Gaussian processes with functional inputs"],
"authFullName_s":["Lucas Reding","Andrés F. López-Lopera","François Bachoc"],
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"citationFull_s":"Joachim Bona-Pellissier, François Malgouyres, François Bachoc. Geometry-induced Regularization in Deep ReLU Neural Networks. 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04452356v2\">⟨hal-04452356v2⟩</a>",
"title_s":["Geometry-induced Regularization in Deep ReLU Neural Networks"],
"authFullName_s":["Joachim Bona-Pellissier","François Malgouyres","François Bachoc"],
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"citationRef_s":"<i>Communications in Statistics - Theory and Methods</i>, 2024, <a target=\"_blank\" href=\"https://dx.doi.org/10.1080/03610926.2024.2321185\">⟨10.1080/03610926.2024.2321185⟩</a>",
"citationFull_s":"François Bachoc, Agnès Lagnoux. Posterior contraction rates for constrained deep Gaussian processes in density estimation and classification. <i>Communications in Statistics - Theory and Methods</i>, 2024, <a target=\"_blank\" href=\"https://dx.doi.org/10.1080/03610926.2024.2321185\">⟨10.1080/03610926.2024.2321185⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-03477991v1\">⟨hal-03477991⟩</a>",
"title_s":["Posterior contraction rates for constrained deep Gaussian processes in density estimation and classification"],
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"citationRef_s":"<i>Transactions on Machine Learning Research Journal</i>, 2024",
"citationFull_s":"Julien Demange-Chryst, François Bachoc, Jérôme Morio, Timothé Krauth. Variational autoencoder with weighted samples for high dimensional non-parametric adaptive importance sampling. <i>Transactions on Machine Learning Research Journal</i>, 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04775920v1\">⟨hal-04775920⟩</a>",
"title_s":["Variational autoencoder with weighted samples for high dimensional non-parametric adaptive importance sampling"],
"authFullName_s":["Julien Demange-Chryst","François Bachoc","Jérôme Morio","Timothé Krauth"],
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"citationRef_s":"<i>Journal of Statistical Software</i>, 2024, 109 (5), <a target=\"_blank\" href=\"https://dx.doi.org/10.18637/jss.v109.i05\">⟨10.18637/jss.v109.i05⟩</a>",
"citationFull_s":"José Daniel Betancourt, François Bachoc, Thierry Klein, Déborah Idier, Jérémy Rohmer, et al.. funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs. <i>Journal of Statistical Software</i>, 2024, 109 (5), <a target=\"_blank\" href=\"https://dx.doi.org/10.18637/jss.v109.i05\">⟨10.18637/jss.v109.i05⟩</a>. <a target=\"_blank\" href=\"https://brgm.hal.science/hal-04582236v3\">⟨hal-04582236v3⟩</a>",
"title_s":["funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs"],
"authFullName_s":["José Daniel Betancourt","François Bachoc","Thierry Klein","Déborah Idier","Jérémy Rohmer","Yves Deville"],
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"citationRef_s":"<i>Electronic Journal of Statistics </i>, 2024, 18 (1), <a target=\"_blank\" href=\"https://dx.doi.org/10.1214/24-EJS2252\">⟨10.1214/24-EJS2252⟩</a>",
"citationFull_s":"François Bachoc, Christoph Muehlmann, Klaus Nordhausen, Joni Virta. Large-sample properties of non-stationary source separation for Gaussian signals. <i>Electronic Journal of Statistics </i>, 2024, 18 (1), <a target=\"_blank\" href=\"https://dx.doi.org/10.1214/24-EJS2252\">⟨10.1214/24-EJS2252⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04775922v1\">⟨hal-04775922⟩</a>",
"title_s":["Large-sample properties of non-stationary source separation for Gaussian signals"],
"authFullName_s":["François Bachoc","Christoph Muehlmann","Klaus Nordhausen","Joni Virta"],
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"citationFull_s":"François Bachoc, Tommaso R Cesari, Roberto Colomboni, Andrea Paudice. A Theoretical Framework for Zeroth-Order Budget Convex\r\nOptimization. <i>Transactions on Machine Learning Research Journal</i>, 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04775923v1\">⟨hal-04775923⟩</a>",
"title_s":["A Theoretical Framework for Zeroth-Order Budget Convex Optimization"],
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"citationFull_s":"Christophette Blanchet-Scalliet, Céline Helbert, Delphine Sinoquet, Miguel Munoz Munoz Zuniga, Rodolphe Le Riche, et al.. Activity report ciroquo research & industry consortium. Ecole Centrale de Lyon; Mines Saint-Etienne; Université Toulouse 3 (Paul Sabatier); Stellantis France; BRGM (Bureau de recherches géologiques et minières); CEA; IFP Energies Nouvelles; Institut de Radioprotection et de Sûreté Nucléaire; Storengy; INRIA; CNRS. 2024, pp.1-11. <a target=\"_blank\" href=\"https://hal.science/hal-04661116v1\">⟨hal-04661116⟩</a>",
"title_s":["Activity report ciroquo research & industry consortium"],
"authFullName_s":["Christophette Blanchet-Scalliet","Céline Helbert","Delphine Sinoquet","Miguel Munoz Munoz Zuniga","Rodolphe Le Riche","Didier Rullière","Clémentine Prieur","Olivier Zahm","Josselin Garnier","Dimo Brockhoff","Olivier Roustant","François Bachoc","Luc Pronzato","Yann Richet","Jérémy Rohmer","Frédéric Huguet","David Gaudrie","Amandine Marrel","Baptiste Kerleguer","Thomas Perrillat-Bottonet","Cedric Durantin"],
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"citationRef_s":"<i>Conference on Neural Information Processing Systems</i>, 2024, Vancouver, Canada",
"citationFull_s":"François Bachoc, Nicolò Cesa-Bianchi, Tommaso R Cesari, Roberto Colomboni. Fair online bilateral trade. <i>Conference on Neural Information Processing Systems</i>, 2024, Vancouver, Canada. <a target=\"_blank\" href=\"https://hal.science/hal-04775926v1\">⟨hal-04775926⟩</a>",
"title_s":["Fair online bilateral trade"],
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"citationRef_s":"<i>Nuclear Engineering and Design</i>, 2023, 413, pp.112527. <a target=\"_blank\" href=\"https://dx.doi.org/10.1016/j.nucengdes.2023.112527\">⟨10.1016/j.nucengdes.2023.112527⟩</a>",
"citationFull_s":"Guillaume Damblin, François Bachoc, Sandro Gazzo, Lucia Sargentini, Alberto Ghione. A generalization of the CIRCE method for quantifying input model uncertainty in presence of several groups of experiments. <i>Nuclear Engineering and Design</i>, 2023, 413, pp.112527. <a target=\"_blank\" href=\"https://dx.doi.org/10.1016/j.nucengdes.2023.112527\">⟨10.1016/j.nucengdes.2023.112527⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04455994v1\">⟨hal-04455994⟩</a>",
"title_s":["A generalization of the CIRCE method for quantifying input model uncertainty in presence of several groups of experiments"],
"authFullName_s":["Guillaume Damblin","François Bachoc","Sandro Gazzo","Lucia Sargentini","Alberto Ghione"],
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"citationRef_s":"<i>Information and Inference</i>, 2023, 12 (3), pp.1686-1715. <a target=\"_blank\" href=\"https://dx.doi.org/10.1093/imaiai/iaad010\">⟨10.1093/imaiai/iaad010⟩</a>",
"citationFull_s":"François Bachoc, Fabrice Gamboa, Max Halford, Jean-Michel Loubes, Laurent Risser. Explaining machine learning models using entropic variable projection. <i>Information and Inference</i>, 2023, 12 (3), pp.1686-1715. <a target=\"_blank\" href=\"https://dx.doi.org/10.1093/imaiai/iaad010\">⟨10.1093/imaiai/iaad010⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04775914v1\">⟨hal-04775914⟩</a>",
"title_s":["Explaining machine learning models using entropic variable projection"],
"authFullName_s":["François Bachoc","Fabrice Gamboa","Max Halford","Jean-Michel Loubes","Laurent Risser"],
"halId_s":"hal-04775914",
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"citationRef_s":"<i>Machine Learning</i>, 2023, 112, pp.4431-4493. <a target=\"_blank\" href=\"https://dx.doi.org/10.1007/s10994-023-06355-4\">⟨10.1007/s10994-023-06355-4⟩</a>",
"citationFull_s":"Joachim Bona-Pellissier, François Bachoc, François Malgouyres. Parameter identifiability of a deep feedforward ReLU neural network. <i>Machine Learning</i>, 2023, 112, pp.4431-4493. <a target=\"_blank\" href=\"https://dx.doi.org/10.1007/s10994-023-06355-4\">⟨10.1007/s10994-023-06355-4⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-03501784v2\">⟨hal-03501784v2⟩</a>",
"title_s":["Parameter identifiability of a deep feedforward ReLU neural network"],
"authFullName_s":["Joachim Bona-Pellissier","François Bachoc","François Malgouyres"],
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"citationRef_s":"<i>Journal of Marine Science and Engineering</i>, 2023, 11 (7), pp.1314. <a target=\"_blank\" href=\"https://dx.doi.org/10.3390/jmse11071314\">⟨10.3390/jmse11071314⟩</a>",
"citationFull_s":"Déborah Idier, Jérémy Rohmer, Rodrigo Pedreros, Sylvestre Le Roy, José Betancourt, et al.. Coastal Flood at Gâvres (Brittany, France): A Simulated Dataset to Support Risk Management and Metamodels Development. <i>Journal of Marine Science and Engineering</i>, 2023, 11 (7), pp.1314. <a target=\"_blank\" href=\"https://dx.doi.org/10.3390/jmse11071314\">⟨10.3390/jmse11071314⟩</a>. <a target=\"_blank\" href=\"https://brgm.hal.science/hal-04185752v1\">⟨hal-04185752⟩</a>",
"title_s":["Coastal Flood at Gâvres (Brittany, France): A Simulated Dataset to Support Risk Management and Metamodels Development"],
"authFullName_s":["Déborah Idier","Jérémy Rohmer","Rodrigo Pedreros","Sylvestre Le Roy","José Betancourt","François Bachoc","Sophie Lecacheux"],
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"citationRef_s":"<i>26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)</i>, Apr 2023, Valencia, Spain. <a target=\"_blank\" href=\"https://dx.doi.org/10.48550/arXiv.2210.06574\">⟨10.48550/arXiv.2210.06574⟩</a>",
"citationFull_s":"François Bachoc, Louis Béthune, Alberto González-Sanz, Jean-Michel Loubes. Gaussian Processes on Distributions based on Regularized Optimal Transport. <i>26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)</i>, Apr 2023, Valencia, Spain. <a target=\"_blank\" href=\"https://dx.doi.org/10.48550/arXiv.2210.06574\">⟨10.48550/arXiv.2210.06574⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-03981114v1\">⟨hal-03981114⟩</a>",
"title_s":["Gaussian Processes on Distributions based on Regularized Optimal Transport"],
"authFullName_s":["François Bachoc","Louis Béthune","Alberto González-Sanz","Jean-Michel Loubes"],
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"citationRef_s":"<i>International Journal for Uncertainty Quantification</i>, 2023, 13 (3), <a target=\"_blank\" href=\"https://dx.doi.org/10.1615/Int.J.UncertaintyQuantification.2022043692\">⟨10.1615/Int.J.UncertaintyQuantification.2022043692⟩</a>",
"citationFull_s":"Julien Demange-Chryst, François Bachoc, Jérôme Morio. Shapley effect estimation in reliability-oriented sensitivity analysis with correlated inputs by importance sampling. <i>International Journal for Uncertainty Quantification</i>, 2023, 13 (3), <a target=\"_blank\" href=\"https://dx.doi.org/10.1615/Int.J.UncertaintyQuantification.2022043692\">⟨10.1615/Int.J.UncertaintyQuantification.2022043692⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-03990015v1\">⟨hal-03990015⟩</a>",
"title_s":["Shapley effect estimation in reliability-oriented sensitivity analysis with correlated inputs by importance sampling","Estimation des effets de Shapley en analyse de sensibilité fiabiliste par échantillonnage préférentiel"],
"authFullName_s":["Julien Demange-Chryst","François Bachoc","Jérôme Morio"],
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