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)

Paper ID: 12822
Venue: VLDB
Year: 2022
Pagerank: 5.2910774e-05
Overall Rank: 9,290 | 36.27%
DOI: 10.14778/3514061.3514071

Incoming Non-self Citations Over Time

Authors

1. Yang Li (Kuaishou; Peking University)
2. Yu Shen (Kuaishou; Peking University)
3. Huaijun Jiang (Kuaishou; Peking University)
4. Wentao Zhang (Peking University)
5. Jixiang Li (Kuaishou)
6. Ji Liu (Kuaishou)
7. Ce Zhang (ETH Zurich)
8. Bin Cui (Peking University)

BibTeX Citation

@article{li_vldb22,
        title = {{Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale}},
        author = {Li, Yang and Shen, Yu and Jiang, Huaijun and Zhang, Wentao and Li, Jixiang and Liu, Ji and Zhang, Ce and Cui, Bin},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {6},
        pages = {1256--1265},
        doi = {10.14778/3514061.3514071},
        url = {https://doi.org/10.14778/3514061.3514071},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,881	The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format	2024	SIGMOD	5.2040783e-05
10,827	A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty	2025	VLDB	5.093636e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
112	HoloClean: Holistic Data Repairs with Probabilistic Inference	2017	VLDB	0.00032801121
205	Snorkel: Rapid Training Data Creation with Weak Supervision	2018	VLDB	0.00025235185
498	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00017440583
1,004	Democratizing Data Science through Interactive Curation of ML Pipelines	2019	SIGMOD	0.00012701932
1,157	Cerebro: A Data System for Optimized Deep Learning Model Selection	2020	VLDB	0.00011924049
1,392	Northstar: An Interactive Data Science System	2018	VLDB	0.00010936065
1,756	SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle	2020	CIDR	9.8172465e-05
2,290	ZeroER: Entity Resolution using Zero Labeled Examples	2020	SIGMOD	8.799251e-05
2,584	Complaint-driven Training Data Debugging for Query 2.0	2020	SIGMOD	8.3783546e-05
2,740	Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation	2022	VLDB	8.1855759e-05
2,987	Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations	2019	SIGMOD	7.8907997e-05
3,116	ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases	2021	SIGMOD	7.7390737e-05
3,272	VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition	2021	VLDB	7.5775321e-05
4,118	Rafiki: Machine Learning as an Analytics Service System	2019	VLDB	6.8908973e-05

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