Hippo: Sharing Computations in Hyper-Parameter Optimization
Summary: Hippo exploits shared hyper-parameter sequence prefixes by merging trial stages into a reusable stage tree, coordinated by a critical-path scheduler. It accelerates single and multi-study HPO up to 3.53× and cuts GPU-hours up to 6.77×. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ahnjae Shin (Seoul National University)
- 2. Joo Seong Jeong (Seoul National University)
- 3. Do Yoon Kim (University of Michigan)
- 4. Soyoung Jung (Seoul National University)
- 5. Byung-Gon Chun (FriendliAI; Seoul National University)
BibTeX Citation
@article{shin_vldb22,
title = {{Hippo: Sharing Computations in Hyper-Parameter Optimization}},
author = {Shin, Ahnjae and Jeong, Joo Seong and Kim, Do Yoon and Jung, Soyoung and Chun, Byung-Gon},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {5},
pages = {1038--1052},
doi = {10.14778/3510397.3510402},
url = {https://doi.org/10.14778/3510397.3510402},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,704 | Optimizing Data Pipelines for Machine Learning in Feature Stores | 2023 | VLDB | 6.1146371e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 803 | MRShare: Sharing Across Multiple Queries in MapReduce | 2010 | VLDB | 0.00013899943 |
| 1,569 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010335423 |
| 1,883 | ReStore: Reusing Results of MapReduce Jobs | 2012 | VLDB | 9.5421713e-05 |
| 3,605 | Computation Reuse in Analytics Job Service at Microsoft | 2018 | SIGMOD | 7.2640711e-05 |
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