When analyzing a dataset, one must not only understand the relationship between the data points, but also the underlying structure of the set. The underlying structure of a dataset is generally estimated from the data on hand, leading to assumptions and less accurate predictions. In order to improve structure learning, LLNL scientists have developed an open source software suite called MTL. Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. This software suite can handle any type of data and consists of multitask learning methods and a framework for easy experimentation with machine learning methods, leading to more accurate assumptions and predictions. The MTL software suite has been tested on earth system model outputs and shows that the proposed model outperforms several existing methods for the problem.
MTL-suite, Open-sourced software licensed under the MIT license (LLNL internal case # CP02174)