A0201
Title: Tree boosting for learning density ratios with generalized Bayesian uncertainty quantification
Authors: Naoki Awaya - Waseda University (Japan) [presenting]
Li Ma - Duke University (United States)
Abstract: Learning the density ratio from two samples of observations is a fundamental task for detecting and quantifying differences between two groups. To provide an accurate approximation of density ratios with reasonable computational cost, a variant of the AdaBoost algorithm is proposed, historically used for classification and regression tasks. Similar to the standard AdaBoost, the proposed algorithm sequentially updates the estimate by adding tree-based weak learners, while observations are weighted based on the gap between the current density ratio estimate and group allocation. A novel loss function called the balancing loss is inspired by the commonly used loss in the classification AdaBoost but is tailored to facilitate direct density ratio estimation. Numerical experiments demonstrate that the proposed algorithm outperforms existing approaches in terms of both accuracy and computational efficiency. Additionally, a generalized Bayesian framework is introduced for uncertainty quantification, allowing for the assessment of statistical significance at each observed point.
