Multi-task feature learning A Argyriou, T Evgeniou, M Pontil Advances in Neural Information Processing Systems 19: Proceedings of the …, 2007 | 1835 | 2007 |
Convex multi-task feature learning A Argyriou, T Evgeniou, M Pontil Machine learning 73, 243-272, 2008 | 1763 | 2008 |
A Spectral Regularization Framework for Multi-Task Structure Learning. A Argyriou, CA Micchelli, M Pontil, Y Ying Advances in Neural Information Processing Systems, 2007 | 349* | 2007 |
Combining graph Laplacians for semi--supervised learning A Argyriou, M Herbster, M Pontil Advances in Neural Information Processing Systems 18, 2005 | 196 | 2005 |
Sparse Prediction with the k-Support Norm A Argyriou, R Foygel, N Srebro Advances in Neural Information Processing Systems 2012, 2012 | 185 | 2012 |
When is there a representer theorem? Vector versus matrix regularizers A Argyriou, CA Micchelli, M Pontil The Journal of Machine Learning Research 10, 2507-2529, 2009 | 171 | 2009 |
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 | 151 | 2006 |
Exploiting Unrelated Tasks in Multi-Task Learning BR Paredes, A Argyriou, N Berthouze, M Pontil Fifteenth International Conference on Artificial Intelligence and Statistics …, 2012 | 146 | 2012 |
Learning convex combinations of continuously parameterized basic kernels A Argyriou, CA Micchelli, M Pontil International conference on computational learning theory, 338-352, 2005 | 136 | 2005 |
An algorithm for transfer learning in a heterogeneous environment A Argyriou, A Maurer, M Pontil Machine Learning and Knowledge Discovery in Databases: European Conference …, 2008 | 104 | 2008 |
Efficient first order methods for linear composite regularizers A Argyriou, CA Micchelli, M Pontil, L Shen, Y Xu arXiv preprint arXiv:1104.1436, 2011 | 61 | 2011 |
Regularization, optimization, kernels, and support vector machines JAK Suykens, M Signoretto, A Argyriou CRC Press, 2014 | 57 | 2014 |
Prisma: Proximal iterative smoothing algorithm F Orabona, A Argyriou, N Srebro arXiv preprint arXiv:1206.2372, 2012 | 44 | 2012 |
Microsoft recommenders: Best practices for production-ready recommendation systems A Argyriou, M González-Fierro, L Zhang Companion Proceedings of the Web Conference 2020, 50-51, 2020 | 41 | 2020 |
A unifying view of representer theorems A Argyriou, F Dinuzzo International Conference on Machine Learning, 748-756, 2014 | 31 | 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 | 31 | 2014 |
Learning the graph of relations among multiple tasks A Argyriou, S Clémençon, R Zhang | 18 | 2013 |
A general framework for structured sparsity via proximal optimization L Baldassarre, J Morales, A Argyriou, M Pontil Artificial Intelligence and Statistics, 82-90, 2012 | 18 | 2012 |
In search for a cure: Recommendation with knowledge graph on CORD-19 I Shen, L Zhang, J Lian, CH Wu, MG Fierro, A Argyriou, T Wu Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 10 | 2020 |
On sparsity inducing regularization methods for machine learning A Argyriou, L Baldassarre, CA Micchelli, M Pontil Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, 205-216, 2013 | 10 | 2013 |