Guillaume Obozinski

6.1k total citations · 1 hit paper
38 papers, 2.0k citations indexed

About

Guillaume Obozinski is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, Guillaume Obozinski has authored 38 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computational Mechanics, 9 papers in Computer Vision and Pattern Recognition and 7 papers in Statistics and Probability. Recurrent topics in Guillaume Obozinski's work include Sparse and Compressive Sensing Techniques (12 papers), Statistical Methods and Inference (7 papers) and Medical Image Segmentation Techniques (3 papers). Guillaume Obozinski is often cited by papers focused on Sparse and Compressive Sensing Techniques (12 papers), Statistical Methods and Inference (7 papers) and Medical Image Segmentation Techniques (3 papers). Guillaume Obozinski collaborates with scholars based in Switzerland, France and United States. Guillaume Obozinski's co-authors include Michael I. Jordan, Jean‐Philippe Vert, Laurent Jacob, Rodolphe Jenatton, Julien Mairal, Ben Taskar, Martin J. Wainwright, Francis R. Bach, Francis Bach and Francis Bach and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Genetics and Food Chemistry.

In The Last Decade

Guillaume Obozinski

37 papers receiving 1.9k citations

Hit Papers

Group lasso with overlap and graph lasso 2009 2026 2014 2020 2009 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guillaume Obozinski Switzerland 15 795 700 588 362 258 38 2.0k
Rahul Mazumder United States 12 425 0.5× 325 0.5× 283 0.5× 298 0.8× 149 0.6× 36 1.3k
Holger Höfling United States 3 364 0.5× 341 0.5× 222 0.4× 511 1.4× 225 0.9× 4 1.3k
Kwangmoo Koh United States 7 1.0k 1.3× 401 0.6× 730 1.2× 192 0.5× 119 0.5× 9 2.5k
Yiming Ying United States 25 664 0.8× 1.3k 1.8× 996 1.7× 240 0.7× 107 0.4× 77 2.6k
Taiji Suzuki Japan 22 313 0.4× 947 1.4× 422 0.7× 370 1.0× 90 0.3× 96 1.8k
Sangwoon Yun South Korea 20 1.1k 1.4× 478 0.7× 819 1.4× 120 0.3× 106 0.4× 61 2.3k
Zaïd Harchaoui France 25 174 0.2× 1.4k 2.0× 1.6k 2.8× 272 0.8× 104 0.4× 59 2.7k
Balaji Krishnapuram United States 20 171 0.2× 952 1.4× 568 1.0× 138 0.4× 242 0.9× 42 2.0k

Countries citing papers authored by Guillaume Obozinski

Since Specialization
Citations

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

Fields of papers citing papers by Guillaume Obozinski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guillaume Obozinski

This figure shows the co-authorship network connecting the top 25 collaborators of Guillaume Obozinski. A scholar is included among the top collaborators of Guillaume Obozinski 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 Guillaume Obozinski. Guillaume Obozinski 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.
Bartram, Julian, et al.. (2024). Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains. PLoS Computational Biology. 20(4). e1011964–e1011964. 4 indexed citations
2.
Székely, Enikő, et al.. (2023). Improved extended-range prediction of persistent stratospheric perturbations using machine learning. Weather and Climate Dynamics. 4(2). 287–307. 3 indexed citations
3.
Krymova, Ekaterina, et al.. (2023). Data-driven modeling of beam loss in the LHC. Frontiers in Physics. 10. 1 indexed citations
4.
Harris, Eliza, et al.. (2023). Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry. Structural Chemistry. 34(5). 1935–1950. 2 indexed citations
5.
Techel, Frank, Michele Volpi, Alec van Herwijnen, et al.. (2022). Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland. Natural hazards and earth system sciences. 22(6). 2031–2056. 25 indexed citations
6.
Perraudin, Nathanaël, et al.. (2022). Physics-Guided Machine Learning for the Analysis of Low SNR STEM-EDXS Data. Microscopy and Microanalysis. 28(S1). 2978–2979.
7.
Techel, Frank, Michele Volpi, Alec van Herwijnen, et al.. (2021). Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland. Repository for Publications and Research Data (ETH Zurich). 4 indexed citations
8.
Jiménez‐Esteve, Bernat, et al.. (2021). Emergence of representative signals for sudden stratospheric warmings beyond current predictable lead times. Weather and Climate Dynamics. 2(3). 841–865. 1 indexed citations
9.
Schenk, Michael, M. Giovannozzi, Ekaterina Krymova, et al.. (2021). Modeling Particle Stability Plots for Accelerator Optimization Using Adaptive Sampling. CERN Document Server (European Organization for Nuclear Research). 1923–1926. 2 indexed citations
10.
Xu, Hu, et al.. (2020). Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. International Conference on Learning Representations. 13 indexed citations
11.
Quantin, Catherine, et al.. (2018). Revue Bibliographique des Méthodes de Couplage des Bases de Données : Applications et Perspectives dans le Cas des Données de Santé Publique. HAL (Le Centre pour la Communication Scientifique Directe). 159(3). 79–123. 1 indexed citations
12.
Richard, Émile & Guillaume Obozinski. (2014). Tight convex relaxations for sparse matrix factorization. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
13.
Gronát, Petr, Guillaume Obozinski, Josef Šivic, & Tomáš Pajdla. (2013). Learning and Calibrating Per Location Classifiers for Visual Place Recognition (Open Access). 1 indexed citations
14.
Mairal, Julien, Rodolphe Jenatton, Guillaume Obozinski, & Francis Bach. (2011). Convex and Network Flow Optimization for Structured Sparsity. arXiv (Cornell University). 12(81). 2681–2720. 55 indexed citations
15.
Jenatton, Rodolphe, Julien Mairal, Francis R. Bach, & Guillaume Obozinski. (2010). Proximal Methods for Sparse Hierarchical Dictionary Learning. Food Chemistry. 344. 487–494. 215 indexed citations
16.
Bach, Francis & Guillaume Obozinski. (2010). Other Grants and Activities - European Research Council (ERC)Starting Investigator Researcher grant. 1 indexed citations
17.
Obozinski, Guillaume, Martin J. Wainwright, & Michael I. Jordan. (2010). Support union recovery in high-dimensional multivariate regression. The Annals of Statistics. 39(1). 176 indexed citations
18.
Sankararaman, Sriram, Guillaume Obozinski, Michael I. Jordan, & Eran Halperin. (2009). Genomic privacy and limits of individual detection in a pool. Nature Genetics. 41(9). 965–967. 132 indexed citations
19.
Obozinski, Guillaume, Martin J. Wainwright, & Michael I. Jordan. (2008). High-dimensional support union recovery in multivariate regression. Neural Information Processing Systems. 21. 1217–1224. 13 indexed citations
20.
Obozinski, Guillaume, Martin J. Wainwright, & Michael I. Jordan. (2008). Union support recovery in high-dimensional multivariate regression. 21–26. 26 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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