Cédric Archambeau
Impact in
- Artificial Intelligence top 2%
- Bayesian Methods and Mixture Models
- Gaussian Processes and Bayesian Inference
- Neural Networks and Applications
- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Statistics and Probability top 2%
- Statistical Methods and Inference
Papers in
-
- Bayesian Methods and Mixture Models 12
- Gaussian Processes and Bayesian Inference 10
- Machine Learning and Data Classification 7
- Neural Networks and Applications 6
- Machine Learning and Algorithms 5
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- Advanced Statistical Methods and Models 5
- Co-authors
- Michel VerleysenManfred OpperFrancis R. BachRodolphe JenattonJohn A. LeeGuillaume BouchardDavid T. JonesMassimiliano Pontil
- Journals
- Artificial Intelligence in Medicine (1 paper)Neurocomputing (1 paper)BMC Bioinformatics (1 paper)Bioinformatics (1 paper)Neural Networks (1 paper)
- Partner nations
- United KingdomGermanyUnited States
In The Last Decade
Cédric Archambeau
48 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 122
- Artificial Intelligence 606
- Statistics and Probability 145
- Computer Vision and Pattern Recognition 234
- Signal Processing 108
- Computational Mathematics 5
Countries citing papers authored by Cédric Archambeau
This map shows the geographic impact of Cédric Archambeau'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 Cédric Archambeau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cédric Archambeau more than expected).
Fields of papers citing papers by Cédric Archambeau
This network shows the impact of papers produced by Cédric Archambeau. 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 Cédric Archambeau. The network helps show where Cédric Archambeau may publish in the future.
Co-authors
The 25 scholars most cited alongside Cédric Archambeau, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 1 | |
| 2 | Towards Robust Episodic Meta-Learning | 2021 | 1 |
| 3 | LEEP: A New Measure to Evaluate Transferability of Learned Representations | 2020 | 8 |
| 4 | Scalable Hyperparameter Transfer Learning | 2018 | 25 |
| 5 | Adaptive algorithms for online convex optimization with long-term constraints | 2016 | 45 |
| 6 | 2013 | 8 | |
| 7 | Sparse Bayesian Multi-Task Learning | 2011 | 23 |
| 8 | 2011 | 26 | |
| 9 | 2009 | 77 | |
| 10 | Variational Inference for Diffusion Processes | 2007 | 37 |
| 11 | 2006 | 0 | |
| 12 | Towards Security Limits in Side-Channel Attacks (With an Application to Block Ciphers) | 2006 | 7 |
| 13 | 2006 | 67 | |
| 14 | Towards a Local Separation Performances Estimator Using Common ICA Contrast Functions | 2004 | 3 |
| 15 | Flexible and Robust Bayesian Classification by Finite Mixture Models | 2004 | 9 |
| 16 | 2004 | 6 | |
| 17 | Locally Linear Embedding versus Isotop | 2003 | 16 |
| 18 | On Convergence Problems of the EM Algorithm for Finite Gaussian Mixtures | 2003 | 31 |
| 19 | Fully nonparametric probability density function estimation with finite Gaussian mixture models | 2003 | 17 |
| 20 | Width optimization of the Gaussian kernels in Radial Basis Function Networks | 2002 | 62 |
About Cédric Archambeau
Cédric Archambeau is a scholar working on Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Communication, having authored 49 papers that have together received 1.1k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (12 papers), Gaussian Processes and Bayesian Inference (10 papers), Machine Learning and Data Classification (7 papers), Neural Networks and Applications (6 papers), Advanced Statistical Methods and Models (5 papers), Advanced Multi-Objective Optimization Algorithms (5 papers), Machine Learning and Algorithms (5 papers) and Face and Expression Recognition (4 papers). The work is most often cited by research in Artificial Intelligence (606 citations), Statistics and Probability (145 citations), Computer Vision and Pattern Recognition (234 citations), Signal Processing (108 citations) and Computational Mathematics (5 citations). Cédric Archambeau has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Michel Verleysen, Manfred Opper, Francis R. Bach, Rodolphe Jenatton, John A. Lee, Guillaume Bouchard, David T. Jones, Massimiliano Pontil, Stefano Lise and Balaji Lakshminarayanan. Their work appears in journals such as Artificial Intelligence in Medicine, Neurocomputing, BMC Bioinformatics, Bioinformatics and Neural Networks.
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.