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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.21181 |
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| _version_ | 1866916924920692736 |
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| author | Chen, Ziheng Huang, Jin Cheng, Jiali Guo, Yuchan Wang, Mengjie Morishetti, Lalitesh Nag, Kaushiki Amiri, Hadi |
| author_facet | Chen, Ziheng Huang, Jin Cheng, Jiali Guo, Yuchan Wang, Mengjie Morishetti, Lalitesh Nag, Kaushiki Amiri, Hadi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21181 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FUTURE: Flexible Unlearning for Tree Ensemble Chen, Ziheng Huang, Jin Cheng, Jiali Guo, Yuchan Wang, Mengjie Morishetti, Lalitesh Nag, Kaushiki Amiri, Hadi Machine Learning Artificial Intelligence 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. |
| title | FUTURE: Flexible Unlearning for Tree Ensemble |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.21181 |