  As an effective learning paradigm against insufficient training samples,
Multi-Task Learning (MTL) encourages knowledge sharing across multiple related
tasks so as to improve the overall performance. In MTL, a major challenge
springs from the phenomenon that sharing the knowledge with dissimilar and hard
tasks, known as negative transfer, often results in a worsened performance.
Though a substantial amount of studies have been carried out against the
negative transfer, most of the existing methods only model the transfer
relationship as task correlations, with the transfer across features and tasks
left unconsidered. Different from the existing methods, our goal is to
alleviate negative transfer collaboratively across features and tasks. To this
end, we propose a novel multi-task learning method called Task-Feature
Collaborative Learning (TFCL). Specifically, we first propose a base model with
a heterogeneous block-diagonal structure regularizer to leverage the
collaborative grouping of features and tasks and suppressing inter-group
knowledge sharing. We then propose an optimization method for the model.
Extensive theoretical analysis shows that our proposed method has the following
benefits: (a) it enjoys the global convergence property and (b) it provides a
block-diagonal structure recovery guarantee. As a practical extension, we
extend the base model by allowing overlapping features and differentiating the
hard tasks. We further apply it to the personalized attribute prediction
problem with fine-grained modeling of user behaviors. Finally, experimental
results on both simulated dataset and real-world datasets demonstrate the
effectiveness of our proposed method
