Hippo: Sharing Computations in Hyper-Parameter Optimization
Summary: Hippo reuses computation across hyper-parameter trials, merging common prefixes into a stage tree. A critical-path scheduler and study-management structures enable cross-trial sharing, trimming training time and GPU-hours for single/multi-study workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ahnjae Shin
- 2. Joo Seong Jeong
- 3. Do Yoon Kim
- 4. Soyoung Jung
- 5. Byung-Gon Chun
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,567 | Optimizing Data Pipelines for Machine Learning in Feature Stores | 2023 | VLDB | 5.4305348e-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 |
|---|---|---|---|---|
| 947 | MRShare: Sharing Across Multiple Queries in MapReduce | 2010 | VLDB | 0.00015114576 |
| 1,666 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.0001096361 |
| 2,205 | ReStore: Reusing Results of MapReduce Jobs | 2012 | VLDB | 9.2920002e-05 |
| 4,174 | Computation Reuse in Analytics Job Service at Microsoft | 2018 | SIGMOD | 6.3856219e-05 |
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