François Bachoc

1.1k total citations
53 papers, 571 citations indexed

About

François Bachoc is a scholar working on Artificial Intelligence, Statistics and Probability and Environmental Engineering. According to data from OpenAlex, François Bachoc has authored 53 papers receiving a total of 571 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 18 papers in Statistics and Probability and 14 papers in Environmental Engineering. Recurrent topics in François Bachoc's work include Gaussian Processes and Bayesian Inference (17 papers), Soil Geostatistics and Mapping (14 papers) and Advanced Multi-Objective Optimization Algorithms (13 papers). François Bachoc is often cited by papers focused on Gaussian Processes and Bayesian Inference (17 papers), Soil Geostatistics and Mapping (14 papers) and Advanced Multi-Objective Optimization Algorithms (13 papers). François Bachoc collaborates with scholars based in France, Switzerland and Chile. François Bachoc's co-authors include Nicolas Durrande, Olivier Roustant, Reinhard Furrer, Agnès Lagnoux, Jean‐Marc Martinez, Déborah Idier, Didier Rullière, Jérémy Rohmer, Clément Chevalier and Julien Bect and has published in prestigious journals such as SHILAP Revista de lepidopterología, Biometrika and Information Sciences.

In The Last Decade

François Bachoc

48 papers receiving 553 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
François Bachoc France 13 193 185 146 106 91 53 571
Thierry Klein France 17 121 0.6× 93 0.5× 329 2.3× 73 0.7× 82 0.9× 40 803
Maria Rightley United States 4 140 0.7× 188 1.0× 264 1.8× 65 0.6× 117 1.3× 8 640
Sébastien da Veiga France 13 89 0.5× 101 0.5× 196 1.3× 70 0.7× 58 0.6× 32 566
Yves Deville France 3 130 0.7× 199 1.1× 119 0.8× 42 0.4× 121 1.3× 4 429
Clémentine Prieur France 16 159 0.8× 105 0.6× 330 2.3× 100 0.9× 138 1.5× 61 1.1k
Matthew Plumlee United States 11 128 0.7× 150 0.8× 138 0.9× 16 0.2× 105 1.2× 35 415
Juliane Müller United States 15 274 1.4× 350 1.9× 117 0.8× 176 1.7× 142 1.6× 30 947
J. Sacks United States 12 156 0.8× 279 1.5× 408 2.8× 71 0.7× 230 2.5× 17 900
Olivier Roustant France 16 326 1.7× 589 3.2× 571 3.9× 103 1.0× 348 3.8× 52 1.3k
Agnès Lagnoux France 10 42 0.2× 79 0.4× 313 2.1× 68 0.6× 73 0.8× 21 485

Countries citing papers authored by François Bachoc

Since Specialization
Citations

This map shows the geographic impact of François Bachoc's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by François Bachoc with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites François Bachoc more than expected).

Fields of papers citing papers by François Bachoc

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by François Bachoc. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by François Bachoc. The network helps show where François Bachoc may publish in the future.

Co-authorship network of co-authors of François Bachoc

This figure shows the co-authorship network connecting the top 25 collaborators of François Bachoc. A scholar is included among the top collaborators of François Bachoc based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with François Bachoc. François Bachoc is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bachoc, François, et al.. (2025). Selective inference after convex clustering with ℓ1 penalization. ESAIM Probability and Statistics. 29. 204–242.
2.
Bachoc, François, et al.. (2024). Large-sample properties of non-stationary source separation for Gaussian signals. Electronic Journal of Statistics. 18(1). 1 indexed citations
3.
Idier, Déborah, et al.. (2023). Coastal Flood at Gâvres (Brittany, France): A Simulated Dataset to Support Risk Management and Metamodels Development. Journal of Marine Science and Engineering. 11(7). 1314–1314. 2 indexed citations
4.
Bachoc, François, et al.. (2023). Efficient estimation of multiple expectations with the same sample by adaptive importance sampling and control variates. Statistics and Computing. 33(5). 1 indexed citations
5.
Bachoc, François, et al.. (2023). Explaining machine learning models using entropic variable projection. Information and Inference A Journal of the IMA. 12(3). 1686–1715. 3 indexed citations
6.
Bachoc, François, et al.. (2023). Parameter identifiability of a deep feedforward ReLU neural network. Machine Learning. 112(11). 4431–4493. 3 indexed citations
7.
Rohmer, Jérémy, Déborah Idier, Rémi Thiéblemont, Gonéri Le Cozannet, & François Bachoc. (2022). Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding. Natural hazards and earth system sciences. 22(10). 3167–3182. 2 indexed citations
8.
Bachoc, François, Nicolas Durrande, Didier Rullière, & Clément Chevalier. (2022). Properties and Comparison of Some Kriging Sub-model Aggregation Methods. Mathematical Geosciences. 54(5). 941–977.
9.
Idier, Déborah, et al.. (2021). Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment. Reliability Engineering & System Safety. 218. 108139–108139. 15 indexed citations
10.
Bachoc, François, et al.. (2020). Sequential construction and dimension reduction of Gaussian processes\n under constraints. arXiv (Cornell University). 5 indexed citations
11.
Bachoc, François, et al.. (2019). Asymptotic properties of the maximum likelihood and cross validation\n estimators for transformed Gaussian processes. arXiv (Cornell University). 4 indexed citations
12.
Bachoc, François, et al.. (2018). Composite likelihood estimation for a gaussian process under fixed\n domain asymptotics. arXiv (Cornell University). 6 indexed citations
13.
Bachoc, François, et al.. (2018). Sensitivity indices for independent groups of variables. HAL (Le Centre pour la Communication Scientifique Directe). 11 indexed citations
14.
Bachoc, François, et al.. (2018). Maximum likelihood estimation for Gaussian processes under inequality constraints. arXiv (Cornell University). 1 indexed citations
15.
Bachoc, François, et al.. (2017). Cross-validation estimation of covariance parameters under fixed-domain asymptotics. Journal of Multivariate Analysis. 160. 42–67. 12 indexed citations
16.
Bachoc, François, et al.. (2017). Finite-dimensional Gaussian approximation with linear inequality constraints. arXiv (Cornell University). 48 indexed citations
17.
Furrer, Reinhard, François Bachoc, & Juan Du. (2016). Asymptotic properties of multivariate tapering for estimation and prediction. Journal of Multivariate Analysis. 149. 177–191. 20 indexed citations
18.
Bachoc, François & Reinhard Furrer. (2016). On the smallest eigenvalues of covariance matrices of multivariate\n spatial processes. arXiv (Cornell University). 6 indexed citations
19.
Bachoc, François, et al.. (2015). Hastings-Metropolis algorithm on Markov chains for small-probability estimation. SHILAP Revista de lepidopterología. 48. 276–307. 2 indexed citations
20.
Bachoc, François, et al.. (2013). Calibration and improved prediction of computer models by universal\n Kriging. arXiv (Cornell University). 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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