  Modern search engine ranking pipelines are commonly based on large
machine-learned ensembles of regression trees. We propose LEAR, a novel -
learned - technique aimed to reduce the average number of trees traversed by
documents to accumulate the scores, thus reducing the overall query response
time. LEAR exploits a classifier that predicts whether a document can early
exit the ensemble because it is unlikely to be ranked among the final top-k
results. The early exit decision occurs at a sentinel point, i.e., after having
evaluated a limited number of trees, and the partial scores are exploited to
filter out non-promising documents. We evaluate LEAR by deploying it in a
production-like setting, adopting a state-of-the-art algorithm for ensembles
traversal. We provide a comprehensive experimental evaluation on two public
datasets. The experiments show that LEAR has a significant impact on the
efficiency of the query processing without hindering its ranking quality. In
detail, on a first dataset, LEAR is able to achieve a speedup of 3x without any
loss in NDCG1@0, while on a second dataset the speedup is larger than 5x with a
negligible NDCG@10 loss (< 0.05%).
