Andreas Argyriou - Google Scholar Citations

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Massimiliano PontilIstituto Italiano di Tecnologia & University College LondonVerified email at iit.it
Theodoros EvgeniouINSEADVerified email at insead.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Johan SuykensProfessor, KU LeuvenVerified email at esat.kuleuven.be
Rina Foygel BarberUniversity of ChicagoVerified email at uchicago.edu
Yiming YingDepartment of Mathematics and Statistics, University at Albany, SUNYVerified email at albany.edu
Nadia Bianchi-BerthouzeProfessor Affective Computing and Interaction, University College LondonVerified email at ucl.ac.uk
Bernardino Romera-ParedesGoogle DeepMindVerified email at google.com
Mark HerbsterAssociate Professor in Computer Science, University College LondonVerified email at cs.ucl.ac.uk
Luca BaldassarreLead Data Scientist, Swiss ReVerified email at swissre.com
Lixin ShenProfessor of Mathematics, Syracuse UniversityVerified email at syr.edu
Yuesheng XuProfessor of Data Science & Math, Old Dominion University; Professor Emeritus, Syracuse UniversityVerified email at odu.edu
Francesco DinuzzoPrincipal Scientist at Expedia GroupVerified email at expedia.com
Francesco OrabonaAssistant Professor, Boston UniversityVerified email at orabona.com
Eugene BelilovskyPostdoctoral Fellow at University of MontrealVerified email at iro.umontreal.ca
Matthew BlaschkoKU LeuvenVerified email at esat.kuleuven.be
Gaël VaroquauxResearch director, INRIAVerified email at normalesup.org
Nicolas Vayatisprofesseur des universités, ENS Paris-SaclayVerified email at cmla.ens-cachan.fr
Emile RichardStanfordVerified email at stanford.edu

Andreas Argyriou

Microsoft UK

Verified email at alum.mit.edu - Homepage

Machine Learning Statistical Learning Optimization


Title	Cited by	Year
Convex multi-task feature learning A Argyriou, T Evgeniou, M Pontil Machine learning 73 (3), 243-272, 2008	1294	2008
Multi-task feature learning A Argyriou, T Evgeniou, M Pontil Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007	1232	2007
A Spectral Regularization Framework for Multi-Task Structure Learning. A Argyriou, CA Micchelli, M Pontil, Y Ying Advances in Neural Information Processing Systems, 2007	264*	2007
Combining graph Laplacians for semi--supervised learning A Argyriou, M Herbster, M Pontil Advances in Neural Information Processing Systems, 67-74, 2006	164	2006
Sparse Prediction with the k-Support Norm A Argyriou, R Foygel, N Srebro Advances in Neural Information Processing Systems 2012, 2012	128	2012
A DC-programming algorithm for kernel selection A Argyriou, R Hauser, CA Micchelli, M Pontil Proceedings of the 23rd international conference on Machine learning, 41-48, 2006	128	2006
When is there a representer theorem? Vector versus matrix regularizers A Argyriou, CA Micchelli, M Pontil Journal of Machine Learning Research 10 (Nov), 2507-2529, 2009	126	2009
Learning convex combinations of continuously parameterized basic kernels A Argyriou, CA Micchelli, M Pontil International Conference on Computational Learning Theory, 338-352, 2005	123	2005
Exploiting Unrelated Tasks in Multi-Task Learning BR Paredes, A Argyriou, N Berthouze, M Pontil Fifteenth International Conference on Artificial Intelligence and Statistics …, 2012	93	2012
An algorithm for transfer learning in a heterogeneous environment A Argyriou, A Maurer, M Pontil Joint European Conference on Machine Learning and Knowledge Discovery in …, 2008	84	2008
On spectral learning A Argyriou, CA Micchelli, M Pontil Journal of Machine Learning Research 11 (Feb), 935-953, 2010	52	2010
Efficient first order methods for linear composite regularizers A Argyriou, CA Micchelli, M Pontil, L Shen, Y Xu arXiv preprint arXiv:1104.1436, 2011	46	2011
Prisma: Proximal iterative smoothing algorithm F Orabona, A Argyriou, N Srebro arXiv preprint arXiv:1206.2372, 2012	40	2012
Regularization, optimization, kernels, and support vector machines JAK Suykens, M Signoretto, A Argyriou CRC Press, 2014	22	2014
Hybrid conditional gradient-smoothing algorithms with applications to sparse and low rank regularization A Argyriou, M Signoretto, J Suykens Regularization, Optimization, Kernels, and Support Vector Machines, 53-82, 2014	18	2014
A unifying view of representer theorems A Argyriou, F Dinuzzo International Conference on Machine Learning, 748-756, 2014	17	2014
A general framework for structured sparsity via proximal optimization L Baldassarre, J Morales, A Argyriou, M Pontil Artificial Intelligence and Statistics, 82-90, 2012	16	2012
Learning the graph of relations among multiple tasks A Argyriou, S Clémençon, R Zhang	14	2013
Convex relaxations of penalties for sparse correlated variables with bounded total variation E Belilovsky, A Argyriou, G Varoquaux, M Blaschko Machine Learning 100 (2-3), 533-553, 2015	6	2015
A study of convex regularizers for sparse recovery and feature selection A Argyriou Technical report, 2010	6	2010

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