Summary of Automated Computational Energy Minimization Of Ml Algorithms Using Constrained Bayesian Optimization, by Pallavi Mitra et al.
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
by Pallavi Mitra, Felix Biessmann
First submitted to arxiv on: 8 Jul 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 Bayesian optimization (BO) has been a reliable framework for optimizing black-box objectives, particularly in hyperparameter optimization (HPO) for machine learning (ML) models. While BO excels at maximizing predictive performance on held-out data, recent advancements in model sizes have led to increased energy consumption becoming a crucial consideration. This paper introduces Constrained Bayesian Optimization (CBO), which aims to minimize energy consumption while maintaining acceptable generalization performance. CBO successfully achieves lower energy usage without compromising predictive performance for regression and classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding the best way to optimize machine learning models while using less energy. The goal is to make sure the model performs well on new data, but also uses less power. The researchers developed a new method called Constrained Bayesian Optimization that achieves this balance. |
Keywords
» Artificial intelligence » Classification » Generalization » Hyperparameter » Machine learning » Optimization » Regression
