Pierre Alquier
- Artificial Intelligence top 10%
- Statistics and Probability top 2%
- Computational Mechanics
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Co-authors
- Nial FrielRichard G. EverittBenjamin GuedjGuillaume LecuéMohamed HebiriOlivier WintenbergerXiaoyin LiNicolás Chopin
- Topics
- Statistical Methods and Inference (17 papers)Sparse and Compressive Sensing Techniques (10 papers)Machine Learning and Algorithms (8 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of the American Statistical AssociationPhysical Review A
- Partner nations
- FranceJapanUnited Kingdom
In The Last Decade
Pierre Alquier
27 papers receiving 279 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 163
- Statistics and Probability 142
- Computational Mechanics 47
- Computer Vision and Pattern Recognition 34
- Control and Systems Engineering 29
Countries citing papers authored by Pierre Alquier
This map shows the geographic impact of Pierre Alquier'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 Pierre Alquier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Alquier more than expected).
Fields of papers citing papers by Pierre Alquier
This network shows the impact of papers produced by Pierre Alquier. 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 Pierre Alquier. The network helps show where Pierre Alquier may publish in the future.
Co-authorship network of co-authors of Pierre Alquier
This figure shows the co-authorship network connecting the top 25 collaborators of Pierre Alquier. A scholar is included among the top collaborators of Pierre Alquier 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 Pierre Alquier. Pierre Alquier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 16 | |
| 4 | 1 | |
| 5 | 9 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 10 | |
| 9 | 2 | |
| 10 | 16 | |
| 11 | Non-negative matrix factorization as a pre-processing tool for travelers temporal profiles clustering. | 3 |
| 12 | An Oracle Inequality for Quasi-Bayesian Non-Negative Matrix Factorization | 5 |
| 13 | 13 | |
| 14 | 13 | |
| 15 | Bayesian matrix completion: prior specification and consistency | 2 |
| 16 | 4 | |
| 17 | 5 | |
| 18 | 16 | |
| 19 | 6 | |
| 20 | 5 |
About Pierre Alquier
Pierre Alquier is a scholar working on Statistics and Probability, Signal Processing and Artificial Intelligence, having authored 33 papers that have together received 294 indexed citations. Recurring topics across this work include Statistical Methods and Inference (17 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and Algorithms (8 papers). The work is most often cited by research in Statistics and Probability (142 citations), Artificial Intelligence (163 citations) and Computational Mathematics (3 citations). Pierre Alquier has collaborated with scholars based in France, Japan and United Kingdom. Frequent co-authors include Nial Friel, Richard G. Everitt, Benjamin Guedj, Guillaume Lecué, Mohamed Hebiri, Olivier Wintenberger, Xiaoyin Li, Nicolás Chopin, James Ridgway and Cristina Butucea. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Physical Review A.
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.