  Multi-task learning has shown to significantly enhance the performance of
multiple related learning tasks in a variety of situations. We present the
fused logistic regression, a sparse multi-task learning approach for binary
classification. Specifically, we introduce sparsity inducing penalties over
parameter differences of related logistic regression models to encode
similarity across related tasks. The resulting joint learning task is cast into
a form that lends itself to be efficiently optimized with a recursive variant
of the alternating direction method of multipliers. We show results on
synthetic data and describe the regime of settings where our multi-task
approach achieves significant improvements over the single task learning
approach and discuss the implications on applying the fused logistic regression
in different real world settings.
