Parallel gradient boosting for flexible estimation of conditional distributions
Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years. Among them is the prediction of entire conditional distributions rather than single functionals, which can often be framed as a multi-output regression problem, for example multiple quantile regression. Addressing such problems with classical implementations of boosting is computationally challenging, because usually one base model is trained for each target at every iteration. More efficient variants of boosting have been proposed to speed up training, but they tend to...
| Authors | Rémy Chapelle, Nicolas Vayatis, Bruno Falissard et al. |
|---|---|
| Primary category | stat.ML |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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