Charles A. Micchelli

15.5k total citations · 2 hit papers
214 papers, 8.6k citations indexed

About

Charles A. Micchelli is a scholar working on Computational Mechanics, Applied Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Charles A. Micchelli has authored 214 papers receiving a total of 8.6k indexed citations (citations by other indexed papers that have themselves been cited), including 105 papers in Computational Mechanics, 67 papers in Applied Mathematics and 50 papers in Computer Vision and Pattern Recognition. Recurrent topics in Charles A. Micchelli's work include Advanced Numerical Analysis Techniques (84 papers), Image and Signal Denoising Methods (40 papers) and Mathematical functions and polynomials (26 papers). Charles A. Micchelli is often cited by papers focused on Advanced Numerical Analysis Techniques (84 papers), Image and Signal Denoising Methods (40 papers) and Mathematical functions and polynomials (26 papers). Charles A. Micchelli collaborates with scholars based in United States, Germany and United Kingdom. Charles A. Micchelli's co-authors include Massimiliano Pontil, Wolfgang Dahmen, Yuesheng Xu, H. N. Mhaskar, Theodoros Evgeniou, M. Gasca, T. J. Rivlin, A. S. Cavaretta, Allan Pinkus and Zhongying Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Mathematics of Computation.

In The Last Decade

Charles A. Micchelli

207 papers receiving 7.6k citations

Hit Papers

Interpolation of scattere... 1986 2026 1999 2012 1986 2005 250 500 750

Author Peers

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

Author Last Decade Papers Cites
Charles A. Micchelli 3.7k 2.3k 1.7k 1.7k 1.6k 214 8.6k
Dianne P. O’Leary 1.7k 0.5× 1.2k 0.5× 1.2k 0.7× 1.7k 1.0× 739 0.5× 169 8.4k
Ronald DeVore 5.5k 1.5× 3.9k 1.7× 1.1k 0.6× 1.6k 1.0× 3.7k 2.4× 163 13.0k
Åke Björck 1.6k 0.4× 897 0.4× 770 0.4× 2.2k 1.3× 777 0.5× 50 7.8k
Larry L. Schumaker 5.9k 1.6× 1.6k 0.7× 382 0.2× 1.2k 0.7× 1.1k 0.7× 192 9.3k
G. W. Stewart 1.9k 0.5× 850 0.4× 1.3k 0.7× 4.8k 2.9× 1.1k 0.7× 152 11.5k
Antonin Chambolle 3.7k 1.0× 3.8k 1.6× 560 0.3× 1.9k 1.1× 901 0.6× 134 8.9k
Wolfgang Dahmen 4.5k 1.2× 2.4k 1.1× 232 0.1× 1.9k 1.1× 1.2k 0.8× 202 7.6k
Sanjoy K. Mitter 1.3k 0.4× 880 0.4× 1.3k 0.8× 2.8k 1.7× 849 0.5× 151 12.1k
Charles K. Chui 1.4k 0.4× 2.9k 1.2× 970 0.6× 438 0.3× 1.8k 1.2× 205 7.3k
Amir Beck 6.1k 1.7× 3.6k 1.5× 2.7k 1.6× 1.7k 1.0× 325 0.2× 83 14.3k

Countries citing papers authored by Charles A. Micchelli

Since Specialization
Citations

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

Fields of papers citing papers by Charles A. Micchelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Charles A. Micchelli. 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 Charles A. Micchelli. The network helps show where Charles A. Micchelli may publish in the future.

Co-authorship network of co-authors of Charles A. Micchelli

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

All Works

20 of 20 papers shown
1.
Chen, Zhongying, et al.. (2023). Advances in Computational Mathematics.
2.
Argyriou, Andreas A., Charles A. Micchelli, Massimiliano Pontil, Lixin Shen, & Yuesheng Xu. (2011). Efficient First Order Methods for Linear Composite Regularizers. Syracuse University Libraries (Syracuse University). 5 indexed citations
3.
Argyriou, Andreas A., Charles A. Micchelli, & Massimiliano Pontil. (2010). On Spectral Learning. Journal of Machine Learning Research. 11(31). 935–953. 29 indexed citations
4.
Micchelli, Charles A., et al.. (2010). A Family of Penalty Functions for Structured Sparsity. Neural Information Processing Systems. 23. 1612–1623. 27 indexed citations
5.
Argyriou, Andreas A., Charles A. Micchelli, & Massimiliano Pontil. (2009). When Is There a Representer Theorem? Vector Versus Matrix Regularizers. Journal of Machine Learning Research. 10(87). 2507–2529. 67 indexed citations
6.
Caponnetto, Andrea, Charles A. Micchelli, Massimiliano Pontil, & Yiming Ying. (2008). Universal Multi-Task Kernels. Journal of Machine Learning Research. 9(52). 1615–1646. 78 indexed citations
7.
Argyriou, Andreas A., Massimiliano Pontil, Yiming Ying, & Charles A. Micchelli. (2007). A Spectral Regularization Framework for Multi-Task Structure Learning. UCL Discovery (University College London). 20. 25–32. 148 indexed citations
8.
Micchelli, Charles A. & Massimiliano Pontil. (2005). Learning the Kernel Function via Regularization. Journal of Machine Learning Research. 6(38). 1099–1125. 264 indexed citations
9.
Evgeniou, Theodoros, Charles A. Micchelli, & Massimiliano Pontil. (2005). Learning Multiple Tasks with Kernel Methods. Journal of Machine Learning Research. 6(21). 615–637. 515 indexed citations breakdown →
10.
Micchelli, Charles A. & Massimiliano Pontil. (2004). Kernels for Multi--task Learning. UCL Discovery (University College London). 17. 921–928. 78 indexed citations
11.
Pérez‐Cruz, Fernando, et al.. (2003). Extension of the nu-SVM range for classification. Acta Haematologica. 83(4). 179–196. 13 indexed citations
12.
Micchelli, Charles A., Thomas Sauer, & Yuesheng Xu. (2000). Subdivision schemes for iterated function systems. Proceedings of the American Mathematical Society. 129(6). 1861–1872. 5 indexed citations
13.
Gasca, M., Charles A. Micchelli, & J.M. Peña. (1994). Banded matrices with banded inverses III: p-slanted matrices. 245–268. 3 indexed citations
14.
Mhaskar, H. N. & Charles A. Micchelli. (1993). How to Choose an Activation Function. Neural Information Processing Systems. 6. 319–326. 30 indexed citations
15.
Micchelli, Charles A., et al.. (1990). Convexity and Bernstein polynomials onk-simploids. Acta Mathematicae Applicatae Sinica English Series. 6(1). 50–66. 6 indexed citations
16.
Dahmen, Wolfgang & Charles A. Micchelli. (1988). The number of solutions to linear Diophantine equations and multivariate splines. Transactions of the American Mathematical Society. 308(2). 509–532. 43 indexed citations
17.
Dahmen, Wolfgang & Charles A. Micchelli. (1988). The Number of Solutions to Linear Diophantine Equations and Multivariate Splines. Transactions of the American Mathematical Society. 308(2). 509–509. 13 indexed citations
18.
Dahmen, Wolfgang & Charles A. Micchelli. (1987). Some remarks on ridge functions. Approximation Theory and Its Applications. 3. 139–143. 22 indexed citations
19.
Micchelli, Charles A. & T. J. Rivlin. (1977). Optimal estimation in approximation theory : [proceedings of an International Symposium on Optimal Estimation in Approximation Theory held in Freudenstadt, Federal Republic of Germany, September 27-29, 1976]. Plenum Press eBooks. 1 indexed citations
20.
Micchelli, Charles A.. (1973). Chebyshev Subspaces and Convergence of Positive Linear Operators. Proceedings of the American Mathematical Society. 40(2). 448–448. 5 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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