Efficiently Answering Durability Prediction Queries
Summary: Proposes MLSS for durability queries, enabling efficient stepwise simulation on complex/black-box models. Uses importance splitting to bias toward promising prefixes, automates splitter design, preserving unbiased MC estimates with guarantees. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Junyang Gao (Google)
- 2. Yifan Xu (Amazon)
- 3. Pankaj K. Agarwal (Duke University)
- 4. Jun Yang (Duke University)
BibTeX Citation
@inproceedings{gao_sigmod21,
title = {{Efficiently Answering Durability Prediction Queries}},
author = {Gao, Junyang and Xu, Yifan and Agarwal, Pankaj K. and Yang, Jun},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457305},
url = {https://dl.acm.org/doi/10.1145/3448016.3457305},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,691 | On Reporting Durable Patterns in Temporal Proximity Graphs | 2024 | PODS | 5.3839057e-05 |
| 9,353 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 5.2829539e-05 |
| 10,657 | A Rank-Based Approach to Recommender System’s Top-K Queries with Uncertain Scores | 2025 | SIGMOD | 5.093636e-05 |
| 11,239 | Efficient and Reliable Estimation of Knowledge Graph Accuracy | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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