Nicolas Durrande

687 total citations
19 papers, 282 citations indexed

About

Nicolas Durrande is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Nicolas Durrande has authored 19 papers receiving a total of 282 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 8 papers in Computational Theory and Mathematics and 5 papers in Statistics, Probability and Uncertainty. Recurrent topics in Nicolas Durrande's work include Gaussian Processes and Bayesian Inference (14 papers), Advanced Multi-Objective Optimization Algorithms (8 papers) and Probabilistic and Robust Engineering Design (5 papers). Nicolas Durrande is often cited by papers focused on Gaussian Processes and Bayesian Inference (14 papers), Advanced Multi-Objective Optimization Algorithms (8 papers) and Probabilistic and Robust Engineering Design (5 papers). Nicolas Durrande collaborates with scholars based in France, United Kingdom and Switzerland. Nicolas Durrande's co-authors include Olivier Roustant, David Ginsbourger, François Bachoc, James Hensman, Laurent Carraro, Clément Chevalier, Didier Rullière, Arno Solin, Neil D. Lawrence and Magnus Rattray and has published in prestigious journals such as Journal of Machine Learning Research, Advances in Water Resources and Journal of Multivariate Analysis.

In The Last Decade

Nicolas Durrande

18 papers receiving 274 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nicolas Durrande France 9 125 94 66 39 36 19 282
Bledar A. Konomi United States 9 96 0.8× 76 0.8× 100 1.5× 36 0.9× 23 0.6× 19 269
Youssef Diouane France 8 76 0.6× 114 1.2× 46 0.7× 43 1.1× 16 0.4× 36 284
Scott A. Starks United States 9 100 0.8× 94 1.0× 82 1.2× 39 1.0× 31 0.9× 70 322
Alex Gorodetsky United States 11 58 0.5× 86 0.9× 180 2.7× 29 0.7× 35 1.0× 56 385
Rémi Lafage France 6 39 0.3× 126 1.3× 76 1.2× 41 1.1× 20 0.6× 10 328
Patrick R. Conrad United States 8 94 0.8× 52 0.6× 152 2.3× 16 0.4× 31 0.9× 10 284
Christèle Faure France 6 49 0.4× 136 1.4× 20 0.3× 14 0.4× 16 0.4× 18 359
Jianfeng Zhang China 14 66 0.5× 30 0.3× 15 0.2× 69 1.8× 22 0.6× 44 624
P. Khademi United States 4 17 0.1× 101 1.1× 39 0.6× 20 0.5× 31 0.9× 4 343
Sami Viitanen Finland 5 106 0.8× 96 1.0× 12 0.2× 7 0.2× 18 0.5× 7 247

Countries citing papers authored by Nicolas Durrande

Since Specialization
Citations

This map shows the geographic impact of Nicolas Durrande'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 Nicolas Durrande with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Durrande more than expected).

Fields of papers citing papers by Nicolas Durrande

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicolas Durrande. 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 Nicolas Durrande. The network helps show where Nicolas Durrande may publish in the future.

Co-authorship network of co-authors of Nicolas Durrande

This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Durrande. A scholar is included among the top collaborators of Nicolas Durrande 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 Nicolas Durrande. Nicolas Durrande is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
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.
2.
Adam, Vincent, et al.. (2020). Doubly Sparse Variational Gaussian Processes. International Conference on Artificial Intelligence and Statistics. 2874–2884. 2 indexed citations
3.
Durrande, Nicolas, et al.. (2020). Sparse Gaussian Processes with Spherical Harmonic Features. arXiv (Cornell University). 1. 2793–2802. 1 indexed citations
4.
Durrande, Nicolas, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, & James Hensman. (2019). Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era. arXiv (Cornell University). 2780–2789. 4 indexed citations
5.
Durrande, Nicolas, et al.. (2018). Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila. arXiv (Cornell University). 5 indexed citations
6.
Bachoc, François, et al.. (2017). Finite-dimensional Gaussian approximation with linear inequality constraints. arXiv (Cornell University). 48 indexed citations
7.
Rullière, Didier, Nicolas Durrande, François Bachoc, & Clément Chevalier. (2017). Nested Kriging predictions for datasets with a large number of observations. Statistics and Computing. 28(4). 849–867. 39 indexed citations
8.
Durrande, Nicolas & Rodolphe Le Riche. (2017). Introduction to Gaussian Process Surrogate Models. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
9.
Durrande, Nicolas, et al.. (2017). Single and multiple crack localization in beam-like structures using a Gaussian process regression approach. Journal of Vibration and Control. 24(18). 4160–4175. 16 indexed citations
10.
Durrande, Nicolas, James Hensman, Magnus Rattray, & Neil D. Lawrence. (2016). Detecting periodicities with Gaussian processes. PeerJ Computer Science. 2. e50–e50. 23 indexed citations
11.
Hensman, James, Nicolas Durrande, & Arno Solin. (2016). Variational Fourier features for Gaussian processes. Journal of Machine Learning Research. 18(1). 5537–5588. 28 indexed citations
12.
Gherlone, Marco, et al.. (2015). Damage localisation in delaminated composite plates using a Gaussian process approach. Meccanica. 50(10). 2537–2546. 9 indexed citations
13.
Ginsbourger, David, Olivier Roustant, & Nicolas Durrande. (2015). On degeneracy and invariances of random fields paths with applications in Gaussian process modelling. Journal of Statistical Planning and Inference. 170. 117–128. 7 indexed citations
14.
Durrande, Nicolas, James Hensman, Magnus Rattray, & Neil D. Lawrence. (2013). Gaussian process models for periodicity detection. arXiv (Cornell University). 1 indexed citations
15.
Durrande, Nicolas, David Ginsbourger, Olivier Roustant, & Laurent Carraro. (2012). ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis. Journal of Multivariate Analysis. 115. 57–67. 37 indexed citations
16.
Ginsbourger, David, et al.. (2012). Distance-based kriging relying on proxy simulations for inverse conditioning. Advances in Water Resources. 52. 275–291. 26 indexed citations
17.
Durrande, Nicolas, David Ginsbourger, & Olivier Roustant. (2011). ADDITIVE COVARIANCE KERNELS FOR HIGH-DIMENSIONAL GAUSSIAN PROCESS MODELING. arXiv (Cornell University). 32 indexed citations
18.
Durrande, Nicolas, David Ginsbourger, Olivier Roustant, & Laurent Carraro. (2011). Reproducing kernels for spaces of zero mean functions. Application to sensitivity analysis. 1 indexed citations
19.
Durrande, Nicolas, David Ginsbourger, & Olivier Roustant. (2010). Additive Kernels for High-dimensional Gaussian Process Modeling. 10. 2 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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