{"ID":2877358,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21181","arxiv_id":"2508.21181","title":"FUTURE: Flexible Unlearning for Tree Ensemble","abstract":"Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the \\textit{right to be forgotten}, several unlearning algorithms have been proposed to enable tree ensembles to forget sensitive information. However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets. To address these limitations, we propose FUTURE, a novel unlearning algorithm for tree ensembles. Specifically, we formulate the problem of forgetting samples as a gradient-based optimization task. In order to accommodate non-differentiability of tree ensembles, we adopt the probabilistic model approximations within the optimization framework. This enables end-to-end unlearning in an effective and efficient manner. Extensive experiments on real-world datasets show that FUTURE yields significant and successful unlearning performance.","short_abstract":"Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the \\textit{right to be forgotten}, several unlearning algorithms ha...","url_abs":"https://arxiv.org/abs/2508.21181","url_pdf":"https://arxiv.org/pdf/2508.21181v1","authors":"[\"Ziheng Chen\",\"Jin Huang\",\"Jiali Cheng\",\"Yuchan Guo\",\"Mengjie Wang\",\"Lalitesh Morishetti\",\"Kaushiki Nag\",\"Hadi Amiri\"]","published":"2025-08-28T19:45:36Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}