Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration
Summary: Intermittent human-in-the-loop model selection to bridge AutoML throughput and expert guidance. Cerebro demonstrates the approach on five real-world DL workloads, delivering a scalable mixed-initiative search over model architectures and hyperparameters. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liangde Li (University of California San Diego)
- 2. Supun Nakandala (University of California San Diego)
- 3. Arun Kumar (University of California San Diego)
BibTeX Citation
@article{li_vldb21,
title = {{Intermittent Human-in-the-Loop Model Selection using Cerebro: A Demonstration}},
author = {Li, Liangde and Nakandala, Supun and Kumar, Arun},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2687--2690},
doi = {10.14778/3476311.3476320},
url = {https://doi.org/10.14778/3476311.3476320},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,244 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 5.4587712e-05 |
| 13,375 | Reimagining Deep Learning Systems Through the Lens of Data Systems | 2024 | VLDB | - |
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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 |
|---|---|---|---|---|
| 640 | Materialization Optimizations for Feature Selection Workloads | 2014 | SIGMOD | 0.00015409494 |
| 1,157 | Cerebro: A Data System for Optimized Deep Learning Model Selection | 2020 | VLDB | 0.00011924049 |
| 1,569 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010335423 |
| 8,979 | Cerebro: A Layered Data Platform for Scalable Deep Learning | 2021 | CIDR | 5.3399615e-05 |
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