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
- 2. Hesam Fathi Moghadam
- 3. Ali Moharrer
- 4. Jingxiao Cai
- 5. Nikan Chavoshi
- 6. Venkatanathan Varadarajan
- 7. Sandeep R. Agrawal
- 8. Sam Idicula
- 9. Tomas Karnagel
- 10. Sanjay Jinturkar
- 11. Nipun Agarwal
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,306 | A Scalable AutoML Approach Based on Graph Neural Networks | 2022 | VLDB | 5.5725759e-05 |
| 7,034 | A Neural Database for Differentially Private Spatial Range Queries | 2022 | VLDB | 4.8504321e-05 |
| 7,493 | SubStrat: A Subset-Based Optimization Strategy for Faster AutoML | 2023 | VLDB | 4.7135369e-05 |
| 8,381 | Assassin: an Automatic classification system based on algorithm selection | 2021 | VLDB | 4.526602e-05 |
| 9,469 | Database Gyms | 2023 | CIDR | 4.3304872e-05 |
| 10,252 | CAPS: Cost-Aware ML Pipeline Selection | 2026 | VLDB | 4.1905499e-05 |
| 11,001 | Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems | 2024 | VLDB | 4.1905499e-05 |
| 11,480 | Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study | 2021 | SIGMOD | 4.1905499e-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 |
|---|---|---|---|---|
| 1,393 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 0.00012223372 |
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