L2P: An Algorithm for Estimating Heavy-tailed Outcomes. Many real-world prediction tasks have outcome (a.k.a. target or re- sponse) variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, 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 prob- lem, we introduce Learning to Place (L2P), which exploits the pairwise relationships between instances to learn from a proportion- ally higher number of rare instances. L2P consists of two stages. In Stage 1, L2P learns a pairwise preference classifier: is instance A > instance B?. In Stage 2, L2P learns to place a new instance into an ordinal ranking of known instances. Based on its place- ment, the new instance is then assigned a value for its outcome variable. Experiments on real data show that L2P outperforms com- peting approaches in terms of accuracy and capability to reproduce heavy-tailed outcome distribution. In addition, L2P can provide an interpretable model with explainable outcomes by placing each predicted instance in context with its comparable neighbors