Summary of Liquid Democracy For Low-cost Ensemble Pruning, by Ben Armstrong et al.
Liquid Democracy for Low-Cost Ensemble Pruning
by Ben Armstrong, Kate Larson
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 approach leverages a delegative voting paradigm called liquid democracy to reduce the computational cost of training ensemble models. By identifying and removing redundant classifiers through delegation mechanisms, the incremental training procedure demonstrates significant cost savings compared to training a full ensemble. The technique also avoids weight centralization, leading to higher accuracy than some boosting methods. This work showcases how frameworks from computational social choice literature can be applied to nontraditional domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re exploring ways to make machine learning more efficient! Researchers found that by using “liquid democracy” – a special voting system – they could reduce the time and resources needed to train complex models. They did this by getting rid of unnecessary parts of the model, kind of like how our brains prune unnecessary neural connections as we learn. This new approach is not only faster but also more accurate than some other methods. It’s an example of how ideas from one field can be used to solve problems in another. |
Keywords
* Artificial intelligence * Boosting * Machine learning
