There are several challenges related with this research line. The first one has to do with the nature of the learning problem: It is a NP-hard problem. This means that there is not known polynomial algorithm that can solve all the instances of it. Therefore we will have to find a balance between the efficiency of the developed algorithms and its range of applicability scenarios. A second challenge is the conception of parallel algorithms with theoretical guarantees. In order to deal with high amount of data, one possibility is to straightforward parallelize the sequential algorithms, however this is not usually the most efficient approach. To excel in this field one has to design specific algorithms that run much faster (than parallelized sequential versions) but at the same time lose some of its theoretical properties. The design of parallel learning algorithms trying to maximize speed and minimize theoretical guarantees loss is a challenge research line.