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Valentina Fedorova

2 accepted papers

2016

Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning

ICML 2016poster

Labeled training data is an essential part of any supervised machine learning framework. In practice, there is a trade-off between the quality of a label and its cost. In this paper, we consider a problem of learning to rank on a large-scale dataset with low-quality relevance labels aiming at maximi…

2015

Large-scale probabilistic predictors with and without guarantees of validity

NeurIPS 2015poster

This paper studies theoretically and empirically a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient. The price to pay for perfect calibration is that these probabilistic…

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