Christophe Biernacki

3.6k total citations · 1 hit paper
52 papers, 2.2k citations indexed

About

Christophe Biernacki is a scholar working on Artificial Intelligence, Statistics and Probability and Signal Processing. According to data from OpenAlex, Christophe Biernacki has authored 52 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 22 papers in Statistics and Probability and 8 papers in Signal Processing. Recurrent topics in Christophe Biernacki's work include Bayesian Methods and Mixture Models (35 papers), Advanced Clustering Algorithms Research (14 papers) and Statistical Methods and Bayesian Inference (11 papers). Christophe Biernacki is often cited by papers focused on Bayesian Methods and Mixture Models (35 papers), Advanced Clustering Algorithms Research (14 papers) and Statistical Methods and Bayesian Inference (11 papers). Christophe Biernacki collaborates with scholars based in France, Canada and Belgium. Christophe Biernacki's co-authors include Gilles Celeux, G. Govaert, Gérard Govaert, Julien Jacques, Isabelle Thomas, Pierre Frankhauser, Matthieu Marbac, Stéphane Chrétien, Emil Eirola and Amaury Lendasse and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Biometrics and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Christophe Biernacki

50 papers receiving 2.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Assessing a mixture model for clustering with the integrated completed likelihood

2000 926 citations Christophe Biernacki, Gilles Celeux et al. profile →

Peers

Christophe Biernacki

AI

SP

MB

CVPR

Christian Hennig United Kingdom

Maria L. Rizzo United States

Jonas Peters Germany

Yuhong Yang United States

Tim Hesterberg United States

Alan Julian Izenman United States

Gérard Govaert France

Joe Whittaker United Kingdom

Thriyambakam Krishnan Netherlands

Hugh Chipman Canada

Christian Hennig United Kingdom

Christophe Biernacki

935 ×0.7

AI

433 ×0.8

SP

170 ×0.6

SP

265 ×1.2

MB

174 ×1.0

CVPR

Citations per year, relative to Christophe Biernacki Christophe Biernacki (= 1×) peers Christian Hennig

Countries citing papers authored by Christophe Biernacki

Since Specialization

Citations

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

Fields of papers citing papers by Christophe Biernacki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christophe Biernacki

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

All Works

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20 of 20 papers shown

1.

Marbac, Matthieu, et al.. (2024). Model-based clustering with missing not at random data. Statistics and Computing. 34(4). 3 indexed citations

2.

Biernacki, Christophe, Julien Jacques, & Christine Keribin. (2023). A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges. Journal of Classification. 40(2). 332–381. 4 indexed citations

3.

Zhang, Luxin, Pascal Germain, Yacine Kessaci, & Christophe Biernacki. (2022). Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 9057–9065. 2 indexed citations

4.

Jacques, Julien, et al.. (2020). Textual data summarization using the Self-Organized Co-Clustering model. Pattern Recognition. 103. 107315–107315. 12 indexed citations

5.

Souami, D., et al.. (2020). On the local and global properties of gravitational spheres of influence. Monthly Notices of the Royal Astronomical Society. 496(4). 4287–4297. 5 indexed citations

6.

Jacques, Julien, et al.. (2019). Model-based co-clustering for mixed type data. Computational Statistics & Data Analysis. 144. 106866–106866. 17 indexed citations

7.

Marbac, Matthieu, et al.. (2015). Model-based clustering for conditionally correlated categorical data. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations

8.

Biernacki, Christophe & Julien Jacques. (2015). Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm. Statistics and Computing. 26(5). 929–943. 25 indexed citations

9.

Eirola, Emil, et al.. (2013). Mixture of Gaussians for distance estimation with missing data. Neurocomputing. 131. 32–42. 45 indexed citations

10.

Biernacki, Christophe, et al.. (2012). A predictive deviance criterion for selecting a generative model in semi-supervised classification. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

11.

Biernacki, Christophe & Julien Jacques. (2010). Modèles génératifs de rangs relatifs à un algorithme de tri par insertion. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations

12.

Biernacki, Christophe & Julien Jacques. (2009). A generative model for rank data based on sorting algorithm. 4 indexed citations

13.

Biernacki, Christophe. (2009). Pourquoi les modeles de melange pour la classification. 40. 1–22.

14.

Biernacki, Christophe. (2007). Degeneracy in the Maximum Likelihood Estimation of Univariate Gaussian Mixtures for Grouped Data and Behaviour of the EM Algorithm. Scandinavian Journal of Statistics. 34(3). 569–586. 5 indexed citations

15.

Biernacki, Christophe. (2005). Testing for a Global Maximum of the Likelihood. Journal of Computational and Graphical Statistics. 14(3). 657–674. 4 indexed citations

16.

Biernacki, Christophe. (2004). Initializing EM using the properties of its trajectories in Gaussian mixtures. Statistics and Computing. 14(3). 267–279. 16 indexed citations

17.

Biernacki, Christophe, et al.. (2002). A Generalized Discriminant Rule When Training Population and Test Population Differ on Their Descriptive Parameters. Biometrics. 58(2). 387–397. 11 indexed citations

18.

Biernacki, Christophe, Gilles Celeux, & Gérard Govaert. (2001). Strategies for Getting the Highest Likelihood in Mixture Models. OpenGrey (Institut de l'Information Scientifique et Technique). 5 indexed citations

19.

Biernacki, Christophe & Gérard Govaert. (1999). Choosing models in model-based clustering and discriminant analysis. Journal of Statistical Computation and Simulation. 64(1). 49–71. 95 indexed citations

20.

Biernacki, Christophe, Gilles Celeux, & Gérard Govaert. (1998). Assessing a Mixture Model for Clustering with the Integrated Classification Likelihood. OpenGrey (Institut de l'Information Scientifique et Technique). 66 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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