Oracle AutoML: A Fast and Predictive AutoML Pipeline
Summary: Oracle AutoML: a fast, iteration-free AutoML pipeline for predictive models. Feed-forward with metalearned proxy models predicts pipeline performance, training only the best candidate and beating H2O/Auto-sklearn on speed while preserving accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anatoly Yakovlev (Oracle)
- 2. Hesam Fathi Moghadam (Oracle)
- 3. Ali Moharrer (Oracle)
- 4. Jingxiao Cai (Oracle)
- 5. Nikan Chavoshi (Oracle)
- 6. Venkatanathan Varadarajan (Oracle)
- 7. Sandeep R. Agrawal (Oracle)
- 8. Sam Idicula (Oracle)
- 9. Tomas Karnagel (Oracle)
- 10. Sanjay Jinturkar (Oracle)
- 11. Nipun Agarwal (Oracle)
BibTeX Citation
@article{yakovlev_vldb20,
title = {{Oracle AutoML: A Fast and Predictive AutoML Pipeline}},
author = {Yakovlev, Anatoly and Moghadam, Hesam Fathi and Moharrer, Ali and Cai, Jingxiao and Chavoshi, Nikan and Varadarajan, Venkatanathan and Agrawal, Sandeep R. and Idicula, Sam and Karnagel, Tomas and Jinturkar, Sanjay and Agarwal, Nipun},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {3166--3180},
doi = {10.14778/3415478.3415542},
url = {https://doi.org/10.14778/3415478.3415542},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,901 | A Scalable AutoML Approach Based on Graph Neural Networks | 2022 | VLDB | 6.0467725e-05 |
| 6,804 | A Neural Database for Differentially Private Spatial Range Queries | 2022 | VLDB | 5.768025e-05 |
| 7,127 | SubStrat: A Subset-Based Optimization Strategy for Faster AutoML | 2023 | VLDB | 5.6957765e-05 |
| 8,574 | Assassin: an Automatic claSSificAtion system baSed on algorithm SelectIoN | 2021 | VLDB | 5.410068e-05 |
| 9,536 | Database Gyms | 2023 | CIDR | 5.2529727e-05 |
| 10,540 | CAPS: Cost-Aware ML Pipeline Selection | 2026 | VLDB | 5.093636e-05 |
| 11,209 | Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems | 2024 | VLDB | 5.093636e-05 |
| 11,674 | Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study | 2021 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,029 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 9.2843642e-05 |
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