Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
Summary: Hyper-Tune is a distributed hyper-parameter tuning system for scalable ML. Automatic resource allocation, asynchronous scheduling, and a multi-fidelity optimizer give 11.2x/5.1x speedups vs BOHB/A-BOHB on XGBoost, CNNs, RNNs, and neural architectures. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yang Li
- 2. Yu Shen
- 3. Huaijun Jiang
- 4. Wentao Zhang
- 5. Jixiang Li
- 6. Ji Liu
- 7. Ce Zhang
- 8. Bin Cui
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,806 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 4.2805224e-05 |
| 10,560 | A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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