Learning multiple tasks using manifold regularization, Advances in Neural Information Processing Systems, pp.46-54, 2010. ,
A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, vol.6, pp.1817-1853, 2005. ,
Convex multi-task feature learning, Machine Learning, pp.243-272, 2008. ,
DOI : 10.1007/s10994-007-5040-8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.2025
An Algorithm for Transfer Learning in a Heterogeneous Environment, European Conference on Machine Learning, 2008. ,
DOI : 10.1007/978-3-540-87479-9_23
Learning Convex Combinations of Continuously Parameterized Basic Kernels, Proceedings of the Eighteenth Conference on Learning Theory, pp.338-352, 2005. ,
DOI : 10.1007/11503415_23
When is there a representer theorem? Vector versus matrix regularizers, Journal of Machine Learning Research, vol.10, pp.2507-2529, 2009. ,
Task clustering and gating for bayesian multi?task learning, Journal of Machine Learning Research, vol.4, pp.83-99, 2003. ,
A general framework for structured sparsity via proximal optimization, International Conference on Artificial Intelligence and Statistics, pp.82-90, 2012. ,
Convex Analysis and Monotone Operator Theory in Hilbert Spaces, CMS Books in Mathematics, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00643354
A model for inductive bias learning, Journal of Artificial Intelligence Research, vol.12, pp.149-198, 2000. ,
Exploiting Task Relatedness for Multiple Task Learning, Proceedings of the Sixteenth Annual Conference on Learning Theory, pp.567-580, 2003. ,
DOI : 10.1007/978-3-540-45167-9_41
Universal multi-task kernels, The Journal of Machine Learning Research, vol.9, pp.1615-1646, 2008. ,
Multi?task learning, Machine Learning, pp.41-75, 1997. ,
Boosted multi-task learning, Machine Learning, vol.8, issue.1, pp.149-173, 2011. ,
DOI : 10.1007/s10994-010-5231-6
Learning multiple tasks with kernel methods, Journal of Machine Learning Research, vol.6, pp.615-637, 2005. ,
A rank minimization heuristic with application to minimum order system approximation, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), pp.4734-4739, 2001. ,
DOI : 10.1109/ACC.2001.945730
Clustered multi-task learning: a convex formulation, Advances in Neural Information Processing Systems 21, pp.745-752, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00320573
Learning with whom to share in multi-task feature learning, Proceedings of the 28th International Conference on Machine Learning, pp.521-528, 2011. ,
A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines, The Annals of Mathematical Statistics, vol.41, issue.2, pp.495-502, 1970. ,
DOI : 10.1214/aoms/1177697089
Learning task grouping and overlap in multi-task learning, Proceedings of the 29th International Conference on Machine Learning, pp.1383-1390, 2012. ,
Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, vol.5, pp.27-72, 2004. ,
SLEP: Sparse Learning with Efficient Projections, 2009. ,
The Rademacher Complexity of Linear Transformation Classes, Proceedings of the 19th Annual Conference on Learning Theory (COLT), volume 4005 of LNAI, pp.65-78, 2006. ,
DOI : 10.1007/11776420_8
A trace inequality of John von Neumann, Monatshefte f ur Mathematik, pp.303-306, 1975. ,
DOI : 10.1007/BF01647331
Learning Bounds for Support Vector Machines with Learned Kernels, Proceedings of the Nineteenth Conference on Learning Theory, pp.169-183, 2006. ,
DOI : 10.1007/11776420_15
Maximum-margin matrix factorization, Advances in Neural Information Processing Systems 17, pp.1329-1336, 2005. ,
Generalization bounds for learning the kernel, Proceedings of the 22nd Annual Conference on Learning Theory, 2009. ,