Gersende Fort

2.1k total citations
61 papers, 1.0k citations indexed

About

Gersende Fort is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics. According to data from OpenAlex, Gersende Fort has authored 61 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Statistics and Probability, 20 papers in Artificial Intelligence and 15 papers in Mathematical Physics. Recurrent topics in Gersende Fort's work include Markov Chains and Monte Carlo Methods (36 papers), Stochastic processes and statistical mechanics (15 papers) and Statistical Methods and Inference (14 papers). Gersende Fort is often cited by papers focused on Markov Chains and Monte Carlo Methods (36 papers), Stochastic processes and statistical mechanics (15 papers) and Statistical Methods and Inference (14 papers). Gersende Fort collaborates with scholars based in France, United Kingdom and Finland. Gersende Fort's co-authors include Éric Moulines, Sophie Lambert‐Lacroix, Randal Douc, Arnaud Guillin, Gareth O. Roberts, Pascal Bianchi, Walid Hachem, Sana Ben Jemaa, Berna Sayrac and Yves F. Atchadé and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and IEEE Transactions on Information Theory.

In The Last Decade

Gersende Fort

58 papers receiving 961 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gersende Fort France 18 419 321 184 135 123 61 1.0k
Chii-Ruey Hwang Taiwan 15 506 1.2× 471 1.5× 205 1.1× 79 0.6× 91 0.7× 39 1.3k
Arnaud Guyader France 14 323 0.8× 271 0.8× 88 0.5× 67 0.5× 81 0.7× 35 840
Stéphane Boucheron France 12 565 1.3× 785 2.4× 182 1.0× 90 0.7× 192 1.6× 22 1.7k
Josef Leydold Austria 17 142 0.3× 239 0.7× 88 0.5× 61 0.5× 116 0.9× 54 955
Günter Last Germany 17 327 0.8× 102 0.3× 399 2.2× 103 0.8× 69 0.6× 87 1.1k
Louis Gordon United States 16 453 1.1× 404 1.3× 272 1.5× 227 1.7× 77 0.6× 27 1.3k
Michele Pavon Italy 22 162 0.4× 287 0.9× 90 0.5× 86 0.6× 80 0.7× 86 1.5k
J. E. Yukich United States 18 404 1.0× 314 1.0× 334 1.8× 65 0.5× 81 0.7× 65 1.2k
Gideon Schechtman Israel 25 463 1.1× 226 0.7× 980 5.3× 56 0.4× 80 0.7× 100 2.1k
Charles Knessl United States 18 234 0.6× 101 0.3× 188 1.0× 84 0.6× 294 2.4× 171 1.2k

Countries citing papers authored by Gersende Fort

Since Specialization
Citations

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

Fields of papers citing papers by Gersende Fort

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gersende Fort

This figure shows the co-authorship network connecting the top 25 collaborators of Gersende Fort. A scholar is included among the top collaborators of Gersende Fort 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 Gersende Fort. Gersende Fort 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.
Fort, Gersende & Éric Moulines. (2023). Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization. Statistics and Computing. 33(3). 4 indexed citations
2.
Dieuleveut, Aymeric, Gersende Fort, Éric Moulines, & Hoi-To Wai. (2023). Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning. IEEE Transactions on Signal Processing. 71. 3117–3148. 3 indexed citations
3.
Dieuleveut, Aymeric, et al.. (2021). Federated-EM with heterogeneity mitigation and variance reduction. Neural Information Processing Systems. 34. 3 indexed citations
4.
Fort, Gersende, Éric Moulines, & Hoi-To Wai. (2021). Geom-Spider-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization. HAL (Le Centre pour la Communication Scientifique Directe). 3135–3139. 3 indexed citations
5.
Fort, Gersende, Éric Moulines, & Hoi-To Wai. (2020). A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm. Neural Information Processing Systems. 33. 16972–16982. 3 indexed citations
6.
Barrera, David, et al.. (2019). Stochastic approximation schemes for economic capital and risk margin computations*. SHILAP Revista de lepidopterología. 4 indexed citations
7.
Fort, Gersende, Emmanuel Gobet, & Éric Moulines. (2017). MCMC design-based non-parametric regression for rare event. Application to nested risk computations. Monte Carlo Methods and Applications. 23(1). 21–42. 5 indexed citations
8.
Fort, Gersende, Benjamin Jourdain, Tony Lelièvre, & Gabriel Stoltz. (2016). Convergence and efficiency of adaptive importance sampling techniques\n with partial biasing. arXiv (Cornell University). 4 indexed citations
9.
Durmus, Alain, et al.. (2016). Subgeometric rates of convergence in Wasserstein distance for Markov chains. Annales de l Institut Henri Poincaré Probabilités et Statistiques. 52(4). 3 indexed citations
10.
Fort, Gersende, Benjamin Jourdain, Tony Lelièvre, & Gabriel Stoltz. (2015). Self-healing umbrella sampling: convergence and efficiency. Statistics and Computing. 27(1). 147–168. 9 indexed citations
11.
Fort, Gersende, et al.. (2014). Convergence of Markovian Stochastic Approximation with discontinuous dynamics. 7 indexed citations
12.
Durmus, Alain, Gersende Fort, & Éric Moulines. (2014). Subgeometric rates of convergence in Wasserstein distance for Markov\n chains. arXiv (Cornell University). 11 indexed citations
13.
Fort, Gersende, et al.. (2013). Convergence of a Particle-Based Approximation of the Block Online Expectation Maximization Algorithm. ACM Transactions on Modeling and Computer Simulation. 23(1). 1–22. 8 indexed citations
14.
Cappé, Olivier, et al.. (2012). Adaptive Metropolis with Online Relabeling. SPIRE - Sciences Po Institutional REpository. 22. 91–99.
15.
Bianchi, Pascal, Gersende Fort, Walid Hachem, & Jérémie Jakubowicz. (2011). Convergence of a distributed parameter estimator for sensor networks with local averaging of the estimates. 3764–3767. 13 indexed citations
16.
Fort, Gersende, et al.. (2009). State-dependent Foster–Lyapunov criteria for subgeometric convergence of Markov chains. Stochastic Processes and their Applications. 119(12). 4176–4193. 8 indexed citations
17.
Douc, Randal, Gersende Fort, & Arnaud Guillin. (2008). Subgeometric rates of convergence of f-ergodic strong Markov processes. Stochastic Processes and their Applications. 119(3). 897–923. 92 indexed citations
18.
Forbes, Florence & Gersende Fort. (2007). Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields. IEEE Transactions on Image Processing. 16(3). 824–837. 22 indexed citations
19.
Fort, Gersende, Sean Meyn, Éric Moulines, & Pierre Priouret. (2006). ODE methods for skip-free Markov chain stability with applications to MCMC. arXiv (Cornell University). 5 indexed citations
20.
Fort, Gersende & Éric Moulines. (2002). Polynomial ergodicity of Markov transition kernels. Stochastic Processes and their Applications. 103(1). 57–99. 43 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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