SubStrat: A Subset-Based Optimization Strategy for Faster AutoML
Summary: SubStrat: a wrapper that speeds AutoML by optimizing dataset size rather than the configuration search, using a genetic algorithm to find small representative subsets that preserve target characteristics and running AutoML on them. Then refines the found pipeline via a short, restricted AutoML on the full data; across Auto-Sklearn/TPOT/H2O achieves ~76% runtime reduction with ~4.15% average accuracy loss. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,252 | CAPS: Cost-Aware ML Pipeline Selection | 2026 | VLDB | 4.1905499e-05 |
| 10,885 | Datamap-Driven Tabular Coreset Selection for Classifier Training | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 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 |
| 2,122 | SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle | 2020 | CIDR | 9.4905306e-05 |
| 2,164 | Elastic Machine Learning Algorithms in Amazon SageMaker | 2020 | SIGMOD | 9.3953268e-05 |
| 2,386 | Oracle AutoML: A Fast and Predictive AutoML Pipeline | 2020 | VLDB | 8.9167446e-05 |
| 2,845 | VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition | 2021 | VLDB | 8.0301674e-05 |
| 5,306 | A Scalable AutoML Approach Based on Graph Neural Networks | 2022 | VLDB | 5.5725759e-05 |
| 8,381 | Assassin: an Automatic classification system based on algorithm selection | 2021 | VLDB | 4.526602e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,651 | ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse | 2019 | VLDB | 4.4710004e-05 |
| 5,965 | Automatic Data Acquisition for Deep Learning | 2021 | VLDB | 5.2476363e-05 |
| 10,513 | Subgroup Discovery with Small and Alternative Feature Sets | 2025 | SIGMOD | 4.1905499e-05 |
| 10,252 | CAPS: Cost-Aware ML Pipeline Selection | 2026 | VLDB | 4.1905499e-05 |
| 5,244 | Towards Benchmarking Feature Type Inference for AutoML Platforms | 2021 | SIGMOD | 5.6021738e-05 |
| 8,253 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD | 4.5444167e-05 |
| 2,845 | VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition | 2021 | VLDB | 8.0301674e-05 |
| 5,306 | A Scalable AutoML Approach Based on Graph Neural Networks | 2022 | VLDB | 5.5725759e-05 |
| 2,386 | Oracle AutoML: A Fast and Predictive AutoML Pipeline | 2020 | VLDB | 8.9167446e-05 |
| 4,962 | Doing More with Less: Characterizing Dataset Downsampling for AutoML | 2021 | VLDB | 5.7979872e-05 |