Snapcase – Regain Control over Your Predictions with Low-Latency Machine Unlearning
Summary: Snapcase recasts recommender unlearning as materialized-view maintenance, achieving sub-second updates over 33M purchases. Differential Dataflow and a custom top-k structure for sparse matrix multiplication remove sensitive interactions—and their influence on other users’ predictions. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Sebastian Schelter (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 2. Stefan Grafberger (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
- 3. Maarten de Rijke (University of Amsterdam)
BibTeX Citation
@article{schelter_vldb24,
title = {{Snapcase – Regain Control over Your Predictions with Low-Latency Machine Unlearning}},
author = {Schelter, Sebastian and Grafberger, Stefan and de Rijke, Maarten},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4273--4276},
doi = {10.14778/3685800.3685853},
url = {https://doi.org/10.14778/3685800.3685853},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 455 | Differential dataflow | 2013 | CIDR | 0.00018133241 |
| 1,340 | Responsible Data Management | 2020 | VLDB | 0.00011111667 |
| 3,960 | HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning | 2021 | SIGMOD | 6.9878154e-05 |
| 4,534 | Shared Arrangements: practical inter-query sharing for streaming dataflows | 2020 | VLDB | 6.6420049e-05 |
| 6,188 | "Amnesia" - A Selection of Machine Learning Models That Can Forget User Data Very Fast | 2020 | CIDR | 5.9483532e-05 |
| 7,381 | DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning | 2023 | SIGMOD | 5.6288368e-05 |
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