Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
<|reference_start|>Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning: Gradient boosting is a sequential ensemble method that fits a new weaker learner to pseudo residuals at each iteration. We propose Wasserstein gradient boosting, a novel extension of gradient boosting that fits a new weak learner to alternative pseudo residuals that are Wasserstein gradients of loss functionals of probability distributions assigned at each input. It solves distribution-valued supervised learning, where the output values of the training dataset are probability distributions for each input. In classification and regression, a model typically returns, for each input, a point estimate of a parameter of a noise distribution specified for a response variable, such as the class probability parameter of a categorical distribution specified for a response label. A main application of Wasserstein gradient boosting in this paper is tree-based evidential learning, which returns a distributional estimate of the response parameter for each input. We empirically demonstrate the superior performance of the probabilistic prediction by Wasserstein gradient boosting in comparison with existing uncertainty quantification methods.<|reference_end|>
@article{matsubara2024wasserstein,
    title={Wasserstein Gradient Boosting: A Framework for Distribution-Valued
  Supervised Learning},
    author={Takuo Matsubara},
    journal={arXiv preprint arXiv:2405.09536},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09536},
    primaryClass={stat.ME cs.LG stat.ML}
}