# FUTURE: Flexible Unlearning for Tree Ensemble

arXiv: https://arxiv.org/abs/2508.21181 · HTML: https://mlsanity.com/paper/2508.21181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 12 of 12)

- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Rethinking Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2402.08787/) (2024)
- [Machine Unlearning for Random Forests](https://mlsanity.com/paper/2009.05567/) (2020)
- [Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond](https://mlsanity.com/paper/2502.05374/) (2025)
- [FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles](https://mlsanity.com/paper/1911.12199/) (2019)
- [Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation](https://mlsanity.com/paper/1904.12796/) (2019)
- [Neural Decision Trees](https://mlsanity.com/paper/1702.07360/) (2017)
- [Tool Unlearning for Tool-Augmented LLMs](https://mlsanity.com/paper/2502.01083/) (2025)
- [From None to Severe: Predicting Severity in Movie Scripts](https://mlsanity.com/paper/2109.09276/) (2021)
- [Speech Unlearning](https://mlsanity.com/paper/2506.00848/) (2025)
- [Positive and Risky Message Assessment for Music Products](https://mlsanity.com/paper/2309.10182/) (2023)
- [Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes](https://mlsanity.com/paper/2502.02672/) (2025)

## Built on by (most cited first, 3 of 3)

- [A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning](https://mlsanity.com/paper/2601.09624/) (2026)
- [TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation](https://mlsanity.com/paper/2606.07688/) (2026)
- [Understanding Machine Unlearning Through the Lens of Mode Connectivity](https://mlsanity.com/paper/2607.23970/) (2026)

## Abstract (arXiv)

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 textitright 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.