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A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty
Summary: Systematic study of early-stopping metric choice in HPO/NAS: using training loss (vs. validation loss) in early stages boosts HPO outcomes up to 24.76%. Introduce uncertainty-aware metrics that add up to ~4% extra gain under budget constraints, improving reliability and resource efficiency for scalable early-stopping.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13819
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,569 | 26.55%
- DOI
-
10.14778/3725688.3725689
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 43 |
Models and Issues in Data Stream Systems |
2002 |
PODS |
0.00072660894 |
| 192 |
HoloClean: Holistic Data Repairs with Probabilistic Inference |
2017 |
VLDB |
0.00035692958 |
| 252 |
Snorkel: Rapid Training Data Creation with Weak Supervision |
2018 |
VLDB |
0.00030532082 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 917 |
Democratizing Data Science through Interactive Curation of ML Pipelines |
2019 |
SIGMOD |
0.00015324193 |
| 2,122 |
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle |
2020 |
CIDR |
9.4905306e-05 |
| 2,845 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
8.0301674e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,871 |
MagicScaler: Uncertainty-aware, Predictive Autoscaling |
2023 |
VLDB |
6.6738199e-05 |
| 3,995 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
6.5475871e-05 |
| 4,377 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
6.2331947e-05 |
| 4,745 |
Rafiki: Machine Learning as an Analytics Service System |
2019 |
VLDB |
5.9466323e-05 |
| 4,799 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.9082876e-05 |
| 9,196 |
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale |
2022 |
VLDB |
4.3723457e-05 |
| 11,226 |
Homomorphic Compression: Making Text Processing on Compression Unlimited |
2023 |
SIGMOD |
4.1905499e-05 |
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