Summary of Cost-sensitive Multi-fidelity Bayesian Optimization with Transfer Of Learning Curve Extrapolation, by Dong Bok Lee et al.
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation
by Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park, Juho Lee, Sung Ju Hwang, Hae Beom Lee
First submitted to arxiv on: 28 May 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 The paper proposes a novel approach to Bayesian Optimization (BO) for efficient hyperparameter optimization (HPO), specifically addressing the problem of early-stopping BO when performance improvement is not satisfactory. The authors introduce a utility function that describes the trade-off between cost and performance, allowing for dynamic configuration selection and automatic stopping based on expected utility maximization. Additionally, they improve sample efficiency by incorporating transfer learning into learning curve extrapolation methods, capturing correlations between configurations to develop a surrogate function for multi-fidelity BO. The algorithm is validated on various learning curve datasets, outperforming previous baselines in terms of cost-performance trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in hyperparameter optimization (HPO) called Bayesian Optimization (BO). It’s like trying to find the best combination of settings for a machine learning model without having to try them all. The authors want to make it stop early if it’s not getting better, and they introduce a special function that helps decide which setting to choose next. They also make it more efficient by using ideas from another technique called transfer learning. This new approach is tested on different types of data and does better than previous ways of doing things. |
Keywords
* Artificial intelligence * Early stopping * Hyperparameter * Machine learning * Optimization * Transfer learning
