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: hcecb5b7e7e0c14ba
Venue: SIGMOD
Year: 2021
Pagerank: 6.8133669e-05
Overall Rank: 4,059 | 72.80%
DOI: 10.1145/3448016.3457239

Incoming Non-self Citations Over Time

Authors

1. Sebastian Schelter (University of Amsterdam)
2. Stefan Grafberger (University of Amsterdam)
3. Ted Dunning (Hewlett Packard Enterprise)

BibTeX Citation

@inproceedings{schelter_sigmod21,
        title = {{HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning}},
        author = {Schelter, Sebastian and Grafberger, Stefan and Dunning, Ted},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457239},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457239},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,954	Interpretable Data-Based Explanations for Fairness Debugging	2022	SIGMOD	9.2965173e-05
5,201	Explainable AI: Foundations, Applications, Opportunities for Data Management Research	2022	SIGMOD	6.2175939e-05
6,130	Equitable Data Valuation Meets the Right to Be Forgotten in Model Markets	2023	VLDB	5.8595306e-05
7,546	DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning	2023	SIGMOD	5.4817498e-05
7,830	Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward	2021	VLDB	5.4294933e-05
10,017	The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format	2024	SIGMOD	5.0825892e-05
11,674	Snapcase – Regain Control over Your Predictions with Low-Latency Machine Unlearning	2024	VLDB	4.9605343e-05

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
14	MonetDB/X100: Hyper-Pipelining Query Execution	2005	CIDR	0.00063972112
289	Implementing Database Operations Using SIMD Instructions	2002	SIGMOD	0.00021955394
444	Differential dataflow	2013	CIDR	0.0001815754
606	Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask	2018	VLDB	0.00015609823
1,163	Responsible Data Management	2020	VLDB	0.00011709266
2,796	Lethe: A Tunable Delete-Aware LSM Engine	2020	SIGMOD	7.9849253e-05
3,947	Understanding and Benchmarking the Impact of GDPR on Database Systems	2020	VLDB	6.8897508e-05
5,327	Probabilistic Demand Forecasting at Scale	2017	VLDB	6.1639984e-05
6,336	"Amnesia" - A Selection of Machine Learning Models That Can Forget User Data Very Fast	2020	CIDR	5.7929324e-05

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