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README.md

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-matMTL
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+#Readme
 
-Multi Task Learning Package for Matlab
-Code imp
+Multi Task Learning Package for Matlab. Code implementing the work in 
+
+> Ciliberto, Carlo, Tomaso Poggio, and Lorenzo Rosasco. *[Convex Learning of Multiple Tasks and their Structure.](http://arxiv.org/pdf/1504.03101.pdf)* **International Conference on Machine Learning (ICML) 2015.**
+
+
+##Installation & Usage
+
+Just `addpath('learning-machine')` to your MATLAB path. An example of how to use the package is in the file `main.m`.
+
+##A couple of words on the package
+
+1. The goal/scope of our work was to present a general convex framework for multi-task learning, which would allow on one hand to capture several previous approaches proposed in Multi-task Learning (e.g. Argyriou '08, Jacob '09, Zhang '10, Dinuzzo '11, etc.) and on the other hand offer a general (meta) block coordinate strategy to solve problems of this form, with guarantees to converge to the global minimum. The code in this repository consists in the implementation of such a meta-strategy for some of these multi-task settings.
+
+2. The package has been designed with the intention of being be plug-and-play, however it is not yet ready for distribution and unfortunately still not documented. In particular the *parameter selection* routine is available but there's no documentation at all. **TBD!**
+
+###References
+[1] Argyriou, Andreas, Theodoros Evgeniou, and Massimiliano Pontil. "Convex multi-task feature learning." Machine Learning 73.3 (2008): 243-272.
+
+[2] Jacob, Laurent, Jean-philippe Vert, and Francis R. Bach. "Clustered multi-task learning: A convex formulation." Advances in neural information processing systems. 2009.
+
+[3] Zhang, Yu, and Dit-Yan Yeung. "A convex formulation for learning task relationships in multi-task learning." arXiv preprint arXiv:1203.3536 (2012).
+
+[4] Dinuzzo, Francesco, Cheng S. Ong, Gianluigi Pillonetto, and Peter V. Gehler. "Learning output kernels with block coordinate descent." Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011.