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HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning

Summary: HedgeCut: an ensemble of randomized decision trees for low-latency machine unlearning. It supports removing data without retraining via vectorised tree operations, delivering ~100 microseconds unlearning latency and up to 36k predictions/sec, with training time and accuracy comparable to Random Forests. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6130
Venue
SIGMOD
Year
2021
Pagerank
6.7492837e-05
Overall Rank
3,806 | 73.53%
DOI
10.1145/3448016.3457239

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