Fabián Pedregosa

66.0k total citations
12 papers, 76 citations indexed

About

Fabián Pedregosa is a scholar working on Artificial Intelligence, Computational Mechanics and Statistics and Probability. According to data from OpenAlex, Fabián Pedregosa has authored 12 papers receiving a total of 76 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Computational Mechanics and 2 papers in Statistics and Probability. Recurrent topics in Fabián Pedregosa's work include Sparse and Compressive Sensing Techniques (3 papers), Stochastic Gradient Optimization Techniques (3 papers) and Machine Learning and ELM (2 papers). Fabián Pedregosa is often cited by papers focused on Sparse and Compressive Sensing Techniques (3 papers), Stochastic Gradient Optimization Techniques (3 papers) and Machine Learning and ELM (2 papers). Fabián Pedregosa collaborates with scholars based in United States, France and Canada. Fabián Pedregosa's co-authors include Michael Eickenberg, Philippe Ciuciu, Alexandre Gramfort, Bertrand Thirion, Mathieu Blondel, Damien Scieur, Bart van Merriënboer, Cecilia Jiménez‐Sánchez, Antonio Segura‐Carretero and Martin Jaggi and has published in prestigious journals such as NeuroImage, Molecules and Foundations of Computational Mathematics.

In The Last Decade

Fabián Pedregosa

10 papers receiving 74 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fabián Pedregosa United States 5 36 22 21 7 6 12 76
Hongtu Zhu United States 6 24 0.7× 11 0.5× 27 1.3× 4 0.6× 2 0.3× 35 89
Benjamin Charlier France 7 13 0.4× 19 0.9× 30 1.4× 10 1.4× 8 1.3× 10 119
Sigang Yu China 6 35 1.0× 19 0.9× 19 0.9× 3 0.4× 1 0.2× 12 79
Hugh Salimbeni United Kingdom 4 17 0.5× 17 0.8× 15 0.7× 2 0.3× 5 58
E. V. Bouhova-Thacker United Kingdom 3 11 0.3× 17 0.8× 4 0.2× 2 0.3× 2 0.3× 10 93
Mengyu Dai United States 4 32 0.9× 7 0.3× 21 1.0× 5 0.7× 6 52
Jens Rimestad Denmark 5 9 0.3× 20 0.9× 13 0.6× 22 3.1× 10 1.7× 7 246
John Suckling United Kingdom 4 16 0.4× 8 0.4× 18 0.9× 8 1.1× 1 0.2× 4 55
Karthik Gopinath United States 5 25 0.7× 21 1.0× 59 2.8× 8 1.1× 2 0.3× 11 104
J. Samuel Preston United States 4 10 0.3× 10 0.5× 23 1.1× 4 0.6× 6 1.0× 7 74

Countries citing papers authored by Fabián Pedregosa

Since Specialization
Citations

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

Fields of papers citing papers by Fabián Pedregosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabián Pedregosa

This figure shows the co-authorship network connecting the top 25 collaborators of Fabián Pedregosa. A scholar is included among the top collaborators of Fabián Pedregosa 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 Fabián Pedregosa. Fabián Pedregosa 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.
Merriënboer, Bart van, et al.. (2022). Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis. Foundations of Computational Mathematics. 23(2). 597–673. 4 indexed citations
2.
Jiménez‐Sánchez, Cecilia, Fabián Pedregosa, Isabel Borrás‐Linares, Jesús Lozano‐Sánchez, & Antonio Segura‐Carretero. (2021). Identification of Bioactive Compounds of Asparagus officinalis L.: Permutation Test Allows Differentiation among “Triguero” and Hybrid Green Varieties. Molecules. 26(6). 1640–1640. 6 indexed citations
3.
Scieur, Damien & Fabián Pedregosa. (2020). Universal Asymptotic Optimality of Polyak Momentum. International Conference on Machine Learning. 1. 8565–8572. 2 indexed citations
4.
Pedregosa, Fabián & Damien Scieur. (2020). Acceleration through spectral density estimation. International Conference on Machine Learning. 1. 7553–7562. 4 indexed citations
5.
Pedregosa, Fabián, et al.. (2020). Linearly Convergent Frank-Wolfe without Line-Search.. International Conference on Artificial Intelligence and Statistics. 1–10.
6.
Pedregosa, Fabián, et al.. (2019). Information matrices and generalization. arXiv (Cornell University). 4 indexed citations
7.
Gidel, Gauthier, Fabián Pedregosa, & Simon Lacoste-Julien. (2018). Frank-Wolfe Splitting via Augmented Lagrangian Method. International Conference on Artificial Intelligence and Statistics. 1456–1465. 1 indexed citations
8.
Pedregosa, Fabián, et al.. (2018). Variance Reduced Three Operator Splitting. arXiv (Cornell University).
9.
Pedregosa, Fabián, et al.. (2018). Step-Size Adaptivity in Projection-Free Optimization. arXiv (Cornell University). 3 indexed citations
10.
Blondel, Mathieu & Fabián Pedregosa. (2016). Lightning: large-scale linear classification, regression and ranking in Python. Zenodo (CERN European Organization for Nuclear Research). 5 indexed citations
11.
Pedregosa, Fabián, Michael Eickenberg, Philippe Ciuciu, Bertrand Thirion, & Alexandre Gramfort. (2014). Data-driven HRF estimation for encoding and decoding models. NeuroImage. 104. 209–220. 45 indexed citations
12.
Pedregosa, Fabián, et al.. (2014). Automatic pathology classification using a single feature machine learning support - vector machines. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9035. 903524–903524. 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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