Meta-gradient boosted decision tree model for weight and target learning
Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov · International Conference on Machine Learning · 2016
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 maximizing the quality of a trained ranker on a small validation dataset with high-quality ground truth relevance labels. Motivated by the classical Gauss-Markov theorem for the linear regression problem, we formulate the problems of (1) reweighting training instances and (2) remapping learning targets. We propose meta-gradient decision tree learning framework for optimizing weight and target functions by applying gradient-based hyperparameter optimization. Experiments on a large-scale real-world dataset demonstrate that we can significantly improve state-of-the-art machine-learning algorithms by incorporating our framework.