Summary of Hardware Aware Ensemble Selection For Balancing Predictive Accuracy and Cost, by Jannis Maier et al.
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost
by Jannis Maier, Felix Möller, Lennart Purucker
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research introduces a novel approach to Automated Machine Learning (AutoML) that balances predictive accuracy and operational efficiency. Traditional AutoML methods employ post hoc ensembling, which can result in longer inference times, hindering practical deployments. The proposed hardware-aware ensemble selection method integrates inference time into the optimization process, evaluating candidates for both accuracy and efficiency. This enables practitioners to choose from a Pareto front of accurate and efficient ensembles. The study evaluates this approach on 83 classification datasets, demonstrating competitive accuracy and significant improvements in operational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoML is like a shortcut to making machines learn from data. It helps humans make better predictions by combining multiple models together. But sometimes, these combined models take too long to give an answer, which can be a problem. This research makes it possible to choose between different combinations of models that are both good at predicting and fast. They tested this approach on lots of datasets and found it works really well. |
Keywords
» Artificial intelligence » Classification » Inference » Machine learning » Optimization
