Emilie Morvant

620 total citations
10 papers, 69 citations indexed

About

Emilie Morvant is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Health Information Management. According to data from OpenAlex, Emilie Morvant has authored 10 papers receiving a total of 69 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Health Information Management. Recurrent topics in Emilie Morvant's work include Machine Learning and Algorithms (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Imbalanced Data Classification Techniques (4 papers). Emilie Morvant is often cited by papers focused on Machine Learning and Algorithms (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Imbalanced Data Classification Techniques (4 papers). Emilie Morvant collaborates with scholars based in France, Canada and Austria. Emilie Morvant's co-authors include Amaury Habrard, Marc Sebban, Pascal Germain, François Laviolette, Stéphane Ayache, Younès Bennani, Ievgen Redko, Massih-Reza Amini, Aurélien Bellet and Liva Ralaivola and has published in prestigious journals such as Neurocomputing, Machine Learning and Pattern Recognition Letters.

In The Last Decade

Emilie Morvant

10 papers receiving 63 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Emilie Morvant	55	18	6	6	4	10	69
Shagun Sodhani	53 1.0×	30 1.7×	6 1.0×	5 0.8×	5 1.3×	6	66
Mozhdeh Gheini	31 0.6×	22 1.2×	5 0.8×	4 0.7×	6 1.5×	5	74
Fukang Liu	49 0.9×	29 1.6×	5 0.8×	8 1.3×	2 0.5×	29	75
Tri Dao	37 0.7×	20 1.1×	4 0.7×	4 0.7×	2 0.5×	12	74
Barun Patra	83 1.5×	26 1.4×	3 0.5×	7 1.2×	2 0.5×	15	106
Zachary Nado	74 1.3×	33 1.8×	4 0.7×	6 1.0×	4 1.0×	3	102
Marek Wydmuch	71 1.3×	15 0.8×	5 0.8×	8 1.3×	3 0.8×	6	81
Bo-Hsiang Tseng	89 1.6×	18 1.0×	8 1.3×	5 0.8×	3 0.8×	16	102
Jaehong Yoon	81 1.5×	56 3.1×	8 1.3×	4 0.7×	5 1.3×	9	115
Maha Elbayad	68 1.2×	18 1.0×	3 0.5×	8 1.3×	2 0.5×	7	82

Countries citing papers authored by Emilie Morvant

Since Specialization

Citations

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

Fields of papers citing papers by Emilie Morvant

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emilie Morvant

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

All Works

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10 of 10 papers shown

Germain, Pascal, et al.. (2023). A general framework for the practical disintegration of PAC-Bayesian bounds. Machine Learning. 113(2). 519–604. 1 indexed citations

Redko, Ievgen, Emilie Morvant, Amaury Habrard, Marc Sebban, & Younès Bennani. (2020). A survey on domain adaptation theory. arXiv (Cornell University). 10 indexed citations

Habrard, Amaury, et al.. (2020). Metric Learning from Imbalanced Data with Generalization Guarantees. Pattern Recognition Letters. 133. 298–304. 23 indexed citations

Germain, Pascal, Amaury Habrard, François Laviolette, & Emilie Morvant. (2019). PAC-Bayes and domain adaptation. Neurocomputing. 379. 379–397. 10 indexed citations

Morvant, Emilie, et al.. (2018). Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. arXiv (Cornell University). 5 indexed citations

Laviolette, François, et al.. (2016). Risk upper bounds for general ensemble methods with an application to multiclass classification. Neurocomputing. 219. 15–25. 1 indexed citations

Germain, Pascal, Amaury Habrard, François Laviolette, & Emilie Morvant. (2015). PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers PAC-Bayesian Theorems for Multiview Learning. arXiv (Cornell University). 1 indexed citations

Morvant, Emilie. (2014). Domain adaptation of weighted majority votes via perturbed variation-based self-labeling. Pattern Recognition Letters. 51. 37–43. 7 indexed citations

Bellet, Aurélien, Amaury Habrard, Emilie Morvant, & Marc Sebban. (2014). Learning a priori constrained weighted majority votes. Machine Learning. 97(1-2). 129–154. 3 indexed citations

10.

Morvant, Emilie, Amaury Habrard, & Stéphane Ayache. (2012). Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions. Knowledge and Information Systems. 33(2). 309–349. 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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