Summary of The Vizier Gaussian Process Bandit Algorithm, by Xingyou Song et al.
The Vizier Gaussian Process Bandit Algorithm
by Xingyou Song, Qiuyi Zhang, Chansoo Lee, Emily Fertig, Tzu-Kuo Huang, Lior Belenki, Greg Kochanski, Setareh Ariafar, Srinivas Vasudevan, Sagi Perel, Daniel Golovin
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an open-source implementation of Google Vizier, a Bayesian optimization service that has been used to optimize millions of hyperparameters in various research and production systems. The algorithm has undergone significant improvements over the years through collective experiences of multiple research efforts and user feedback. This report delves into the design choices and implementation details of the current default algorithm provided by Open Source Vizier. Experimental results on standardized benchmarks demonstrate its robustness and versatility, outperforming industry baselines in various practical scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special tool called Google Vizier that helps people find the best settings for their computer programs. The tool has been used many times to improve how well programs work. In this report, the team behind Open Source Vizier shares how they designed and built the tool. They tested it on some standard challenges and showed that it performs better than other tools in many situations. |
Keywords
» Artificial intelligence » Optimization
