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)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,867 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 0.00010272055 |
| 4,872 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD | 5.8609352e-05 |
| 6,263 | Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets | 2023 | VLDB | 5.1349507e-05 |
| 7,489 | DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning | 2023 | SIGMOD | 4.7180617e-05 |
| 7,655 | Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward | 2021 | VLDB | 4.6872456e-05 |
| 9,806 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 4.2805224e-05 |
| 11,096 | Snapcase – Regain Control over Your Predictions with Low-Latency Machine Unlearning | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 35 | MonetDB/X100: Hyper-Pipelining Query Execution | 2005 | CIDR | 0.00076197749 |
| 343 | Implementing Database Operations Using SIMD Instructions | 2002 | SIGMOD | 0.00026768139 |
| 522 | Differential dataflow | 2013 | CIDR | 0.00021099241 |
| 853 | Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask | 2018 | VLDB | 0.00015940507 |
| 1,404 | Responsible Data Management | 2020 | VLDB | 0.00012174977 |
| 3,386 | Lethe: A Tunable Delete-Aware LSM Engine | 2020 | SIGMOD | 7.1577103e-05 |
| 4,377 | Understanding and Benchmarking the Impact of GDPR on Database Systems | 2020 | VLDB | 6.2404627e-05 |
| 5,257 | Probabilistic Demand Forecasting at Scale | 2017 | VLDB | 5.6003925e-05 |
| 5,433 | "Amnesia" - A Selection of Machine Learning Models That Can Forget User Data Very Fast | 2020 | CIDR | 5.5051607e-05 |
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