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Aleksandr Vorobev

2 accepted papers

2019

Learning to select for a predefined ranking

ICML 2019oral

In this paper, we formulate a novel problem of learning to select a set of items maximizing the quality of their ordered list, where the order is predefined by some explicit rule. Unlike the classic information retrieval problem, in our setting, the predefined order of items in the list may not corr…

2018

CatBoost: unbiased boosting with categorical features

NeurIPS 2018poster

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBo…