Summary of Identifying Easy Instances to Improve Efficiency Of Ml Pipelines For Algorithm-selection, by Quentin Renau and Emma Hart
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
by Quentin Renau, Emma Hart
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 proposed method identifies easy instances that can be solved quickly using a generalist solver without requiring algorithm-selection. This saves computational resources, allowing for additional function evaluations on harder problems in an algorithm-selection pipeline. The approach is demonstrated on the BBOB dataset in batch and streaming settings, showing substantial savings in function evaluations and improved performance compared to the virtual best solver and single best solver. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found a way to make computer programs work more efficiently. They did this by identifying problems that are easy to solve and can be done quickly using a simple method. This frees up computer resources, which can then be used to solve harder problems. The team tested their idea on a large set of problems and showed that it saves time and makes the program work better. |
