We propose HedgeCut, a classification model based on an ensemble of randomised decision trees, which is designed to answer unlearning requests with low latency.

We focus on supervised classification tasks, and propose a new ML model called HedgeCut, which can efficiently unlearn a small fraction of its training data.

HedgeCut, a classification model based on an ensemble of randomised decision trees, which is designed to answer unlearning requests with low latency is ...

Jun 18, 2021 · We propose HedgeCut, a classification model based on an ensemble of randomised decision trees, which is designed to answer unlearning requests ...

We propose HedgeCut, a classification model based on an ensemble of randomized decision trees, which is designed to answer unlearning requests with low latency.

We focus on supervised classification tasks, and propose a new ML model called HedgeCut, which can efficiently unlearn a small fraction of its training data.

Hedgecut. Source code for our SIGMOD'21 paper on "HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning" https://ssc.io/pdf/rdm235.pdf ...

Mar 15, 2024 · HedgeCut [34] focuses on unlearning requests with low latency in extremely randomized trees. It introduces the concept of split robustness to ...

HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning, 2021, Schelter et al. SIGMOD, HedgeCut, [Code], Tree-based Models. A Unified PAC ...

May 13, 2024 · Hedgecut: Maintaining randomised trees for low-latency machine unlearning. In Proceedings of the 2021 International Conference on Management ...