Aymeric Dieuleveut

803 total citations
12 papers, 56 citations indexed

About

Aymeric Dieuleveut is a scholar working on Artificial Intelligence, Computational Mechanics and Numerical Analysis. According to data from OpenAlex, Aymeric Dieuleveut has authored 12 papers receiving a total of 56 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 7 papers in Computational Mechanics and 3 papers in Numerical Analysis. Recurrent topics in Aymeric Dieuleveut's work include Stochastic Gradient Optimization Techniques (9 papers), Sparse and Compressive Sensing Techniques (7 papers) and Advanced Optimization Algorithms Research (3 papers). Aymeric Dieuleveut is often cited by papers focused on Stochastic Gradient Optimization Techniques (9 papers), Sparse and Compressive Sensing Techniques (7 papers) and Advanced Optimization Algorithms Research (3 papers). Aymeric Dieuleveut collaborates with scholars based in France, Burundi and Hong Kong. Aymeric Dieuleveut's co-authors include Francis Bach, Alain Durmus, Gersende Fort, Éric Moulines, Kumar Kshitij Patel, Adrien Taylor, Hoi-To Wai, Julien M. Hendrickx, Andreas Hug and Martin Jaggi and has published in prestigious journals such as IEEE Transactions on Signal Processing, The Annals of Statistics and Mathematical Programming Computation.

In The Last Decade

Aymeric Dieuleveut

11 papers receiving 54 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aymeric Dieuleveut France	4	38	17	17	9	8	12	56
Nilesh Tripuraneni United States	6	72 1.9×	19 1.1×	22 1.3×	7 0.8×	10 1.3×	11	85
Gauthier Gidel Canada	5	28 0.7×	16 0.9×	5 0.3×	4 0.4×	6 0.8×	12	36
Darina Dvinskikh Russia	6	46 1.2×	41 2.4×	12 0.7×	20 2.2×	13 1.6×	22	81
Michał Dereziński United States	4	24 0.6×	12 0.7×	10 0.6×	8 0.9×	3 0.4×	18	43
Eduard Gorbunov Russia	6	42 1.1×	35 2.1×	5 0.3×	18 2.0×	19 2.4×	14	70
Majid Janzamin United States	7	28 0.7×	20 1.2×	4 0.2×	6 0.7×	3 0.4×	11	79
Filip Hanzely Saudi Arabia	5	27 0.7×	16 0.9×	2 0.1×	10 1.1×	6 0.8×	8	63
Øyvind Ryan Norway	4	16 0.4×	8 0.5×	29 1.7×	4 0.4×	2 0.3×	17	74
Blake Woodworth United States	5	70 1.8×	27 1.6×	2 0.1×	5 0.6×	8 1.0×	9	94
Lechao Xiao United States	7	59 1.6×	5 0.3×	6 0.4×	5 0.6×	4 0.5×	13	113

Countries citing papers authored by Aymeric Dieuleveut

Since Specialization

Citations

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

Fields of papers citing papers by Aymeric Dieuleveut

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aymeric Dieuleveut

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

All Works

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12 of 12 papers shown

Glineur, François, et al.. (2024). PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. Mathematical Programming Computation. 16(3). 337–367. 2 indexed citations

Dieuleveut, Aymeric, et al.. (2023). Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning. arXiv (Cornell University). 9 indexed citations

Dieuleveut, Aymeric, et al.. (2023). On Fundamental Proof Structures in First-Order Optimization. 3023–3030. 2 indexed citations

Dieuleveut, Aymeric, et al.. (2023). Counter-Examples in First-Order Optimization: A Constructive Approach. IEEE Control Systems Letters. 7. 2485–2490. 2 indexed citations

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

Dieuleveut, Aymeric, et al.. (2021). Federated-EM with heterogeneity mitigation and variance reduction. Neural Information Processing Systems. 34. 3 indexed citations

Boyer, Claire, et al.. (2020). Debiasing Averaged Stochastic Gradient Descent to handle missing values. Neural Information Processing Systems. 33. 12957–12967. 1 indexed citations

Dieuleveut, Aymeric, Alain Durmus, & Francis Bach. (2020). Bridging the gap between constant step size stochastic gradient descent and Markov chains. The Annals of Statistics. 48(3). 26 indexed citations

Dieuleveut, Aymeric, et al.. (2020). On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent. arXiv (Cornell University). 1. 7641–7651. 1 indexed citations

10.

Dieuleveut, Aymeric & Kumar Kshitij Patel. (2019). Communication trade-offs for Local-SGD with large step size. Neural Information Processing Systems. 32. 13579–13590. 4 indexed citations

11.

Hug, Andreas, et al.. (2018). Wasserstein is all you need.. 1 indexed citations

12.

Dieuleveut, Aymeric & Francis Bach. (2016). Nonparametric stochastic approximation with large step-sizes. The Annals of Statistics. 44(4). 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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