Mathieu Gerber

406 total citations
12 papers, 121 citations indexed

About

Mathieu Gerber is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Mathieu Gerber has authored 12 papers receiving a total of 121 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Statistics and Probability, 5 papers in Artificial Intelligence and 4 papers in Statistics, Probability and Uncertainty. Recurrent topics in Mathieu Gerber's work include Markov Chains and Monte Carlo Methods (5 papers), Statistical Methods and Inference (4 papers) and Mathematical Approximation and Integration (3 papers). Mathieu Gerber is often cited by papers focused on Markov Chains and Monte Carlo Methods (5 papers), Statistical Methods and Inference (4 papers) and Mathematical Approximation and Integration (3 papers). Mathieu Gerber collaborates with scholars based in United Kingdom, France and United States. Mathieu Gerber's co-authors include Christian P. Robert, Pierre Jacob, Luke Bornn, Christophe Espanet, Luke Bornn, Nicolás Chopin, Daniel Depernet, Nick Whiteley, Frédéric Dubas and Frédéric Dubas and has published in prestigious journals such as Biometrika, Journal of the Royal Statistical Society Series B (Statistical Methodology) and SIAM Journal on Numerical Analysis.

In The Last Decade

Mathieu Gerber

12 papers receiving 119 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mathieu Gerber United Kingdom 4 64 47 19 15 15 12 121
Motonobu Kanagawa Japan 6 16 0.3× 72 1.5× 21 1.1× 25 1.7× 6 0.4× 12 131
Milan Merkle Serbia 8 32 0.5× 14 0.3× 8 0.4× 5 0.3× 7 0.5× 31 228
Ewa Skubalska-Rafajłowicz Poland 7 14 0.2× 27 0.6× 8 0.4× 9 0.6× 2 0.1× 16 83
Zhengcheng Zhang China 9 297 4.6× 18 0.4× 233 12.3× 6 0.4× 7 0.5× 28 334
Jason M. Klusowski United States 7 19 0.3× 81 1.7× 3 0.2× 5 0.3× 10 0.7× 15 131
Christian A. Naesseth Sweden 7 24 0.4× 88 1.9× 9 0.5× 44 2.9× 6 0.4× 16 124
Aolin Xu United States 7 19 0.3× 82 1.7× 4 0.2× 2 0.1× 36 2.4× 17 157
Dhan Jeet Singh India 7 13 0.2× 81 1.7× 3 0.2× 47 3.1× 10 0.7× 15 139
Tengyu Ma China 8 7 0.1× 44 0.9× 1 0.1× 15 1.0× 5 0.3× 21 103
Junier B. Oliva United States 6 12 0.2× 69 1.5× 3 0.2× 12 0.8× 3 0.2× 23 116

Countries citing papers authored by Mathieu Gerber

Since Specialization
Citations

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

Fields of papers citing papers by Mathieu Gerber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mathieu Gerber

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

All Works

12 of 12 papers shown
1.
Chopin, Nicolás & Mathieu Gerber. (2024). Higher-Order Monte Carlo through Cubic Stratification. SIAM Journal on Numerical Analysis. 62(1). 229–247. 1 indexed citations
2.
Alquier, Pierre & Mathieu Gerber. (2023). Universal robust regression via maximum mean discrepancy. Biometrika. 111(1). 71–92. 1 indexed citations
3.
Jacob, Pierre, et al.. (2019). Approximate Bayesian computation with the Wasserstein distance. Journal of the Royal Statistical Society Series A (Statistics in Society). 1 indexed citations
4.
Jacob, Pierre, et al.. (2019). On parameter estimation with the Wasserstein distance. Information and Inference A Journal of the IMA. 8(4). 657–676. 29 indexed citations
5.
Jacob, Pierre, et al.. (2019). Approximate Bayesian Computation with the Wasserstein Distance. Journal of the Royal Statistical Society Series B (Statistical Methodology). 81(2). 235–269. 62 indexed citations
6.
Gerber, Mathieu & Nicolás Chopin. (2017). Convergence of sequential quasi-Monte Carlo smoothing algorithms. Bernoulli. 23(4B). 2 indexed citations
7.
Gerber, Mathieu, et al.. (2017). Coupled circuit and magnetic fast model for high‐speed permanent‐magnet drive design. IET Electrical Systems in Transportation. 8(1). 27–34. 3 indexed citations
8.
Gerber, Mathieu & Nick Whiteley. (2017). Stability with respect to initial conditions in V-norm for nonlinear filters with ergodic observations. Journal of Applied Probability. 54(1). 118–133. 2 indexed citations
9.
Gerber, Mathieu, et al.. (2016). Coupled Electronic and Magnetic Fast Simulation for High-Speed Permanent-Magnet Drive Design. 1–6. 3 indexed citations
10.
Gerber, Mathieu & Luke Bornn. (2016). Improving simulated annealing through derandomization. Journal of Global Optimization. 68(1). 189–217. 3 indexed citations
11.
Gerber, Mathieu & Luke Bornn. (2015). . arXiv (Cornell University). 6 indexed citations
12.
Tavernier, Serge, et al.. (2015). Design of a cost-efficient high-speed high-efficiency PM machine for compressor applications. 1. 3852–3856. 8 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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