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Inferring predictive maps between multiple input and multiple outputvariables or tasks has innumerable applications in data science. Multi-tasklearning attempts to learn the maps to several output tasks simultaneously withinformation sharing between them. We propose a novel multi-task learningframework for sparse linear regression, where a full task hierarchy isautomatically inferred from the data, with the assumption that the taskparameters follow a hierarchical tree structure. The leaves of the tree are theparameters for individual tasks, and the root is the global model thatapproximates all the tasks. We apply the proposed approach to develop andevaluate: (a) predictive models of plant traits using large-scale and automatedremote sensing data, and (b) GWAS methodologies mapping such derived phenotypesin lieu of hand-measured traits. We demonstrate the superior performance of ourapproach compared to other methods, as well as the usefulness of discoveringhierarchical groupings between tasks. Our results suggest that richer geneticmapping can indeed be obtained from the remote sensing data. In addition, ourdiscovered groupings reveal interesting insights from a plant scienceperspective.

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties