Gilles Blanchard

2.8k citations
53 papers · 1.1k indexed · h-index 19

Gilles Blanchard

49 papers receiving 1.0k citations

Peers

Gilles Blanchard
Comparison fields: 5 of 126
  • Statistics and Probability 225
  • Artificial Intelligence 561
  • Computer Vision and Pattern Recognition 257
  • Signal Processing 117
  • Computational Mechanics 133
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Citations per year

Countries citing papers authored by Gilles Blanchard

Since Specialization
Citations

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

Fields of papers citing papers by Gilles Blanchard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Gilles Blanchard, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Gilles Blanchard Line = papers co-authored together Gilles Blanchard links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20231
2 20210
3
Decontamination of Mutual Contamination Models
20191
4
Parallelizing Spectrally Regularized Kernel Algorithms
201818
5 20182
6 20178
7 20163
8 201620
9 201647
10 201512
11 201353
12
Classification with asymmetric label noise: Consistency and maximal denoising
201325
13
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
2011139
14
Kernel Partial Least Squares is Universally Consistent
20106
15 2009115
16
Novelty detection: Unlabeled data definitely help
200930
17 200923
18
Self-consistent multiple testing procedures
20080
19
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction
20054
20 200366

About Gilles Blanchard

Gilles Blanchard is a scholar working on Statistics and Probability, Mathematical Physics and Artificial Intelligence, having authored 53 papers that have together received 1.1k indexed citations. Recurring topics across this work include Statistical Methods and Inference (18 papers), Sparse and Compressive Sensing Techniques (11 papers), Machine Learning and Algorithms (10 papers), Machine Learning and Data Classification (8 papers), Numerical methods in inverse problems (8 papers), Statistical Methods and Bayesian Inference (7 papers), Statistical Methods in Clinical Trials (7 papers) and Blind Source Separation Techniques (5 papers). The work is most often cited by research in Statistics and Probability (225 citations), Artificial Intelligence (561 citations) and Computer Vision and Pattern Recognition (257 citations). Gilles Blanchard has collaborated with scholars based in Germany, France and United States. Frequent co-authors include Clayton Scott, Gyemin Lee, Benjamin Blankertz, Olivier Bousquet, Pascal Massart, Laurent Zwald, Étienne Roquain, Nicolas Vayatis, Gábor Lugosi and Donald Geman. Their work appears in journals such as Scientific Reports, IEEE Transactions on Information Theory and IEEE Transactions on Biomedical Engineering.

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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