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Found 3 papers, 2 papers with code
Which Tricks Are Important for Learning to Rank?
To address these questions, we compare LambdaMART with YetiRank and StochasticRank methods and their modifications.
CatBoost: gradient boosting with categorical features support
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets.
CatBoost: unbiased boosting with categorical features
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit.
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