  Many real-world prediction tasks have outcome variables that have
characteristic heavy-tail distributions. Examples include copies of books sold,
auction prices of art pieces, demand for commodities in warehouses, etc. By
learning heavy-tailed distributions, "big and rare" instances (e.g., the
best-sellers) will have accurate predictions. Most existing approaches are not
dedicated to learning heavy-tailed distribution; thus, they heavily
under-predict such instances. To tackle this problem, we introduce Learning to
Place (L2P), which exploits the pairwise relationships between instances for
learning. In its training phase, L2P learns a pairwise preference classifier:
is instance A > instance B? In its placing phase, L2P obtains a prediction by
placing the new instance among the known instances. Based on its placement, the
new instance is then assigned a value for its outcome variable. Experiments on
real data show that L2P outperforms competing approaches in terms of accuracy
and ability to reproduce heavy-tailed outcome distribution. In addition, L2P
provides an interpretable model by placing each predicted instance in relation
to its comparable neighbors. Interpretable models are highly desirable when
lives and treasure are at stake.
