Author

Nicolas Vayatis

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#machine learning Jul 2026

Parallel gradient boosting for flexible estimation of conditional distributions

A modification of the gradient boosting algorithm, which is called parallel gradient boosting, designed to circumvent all limitations of classical implementations, is studied, which provides predictions of similar quality to state-of-the-art boosting libraries such as XGBoost, while being faster by several orders of ma...

Rémy Chapelle, Nicolas Vayatis, B. Falissard et al. · arXiv.org · 1 citation

#machine learning Preprint Oct 2026

On the Calibration of Crowdsourcing Aggregators

Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly used for making probabilistic decisions. To the best of our knowledge, we provide the first theoretical and empirical study of calibration in label aggregation. To our g...

G. Singer, Reda Arab, Maïssa Frikha et al. · 0 citations