2 papers
cs.IR2021
Learning Early Exit Strategies for Additive Ranking Ensembles
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3
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…
cs.IR2020
Query-level Early Exit for Additive Learning-to-Rank Ensembles
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers…