Summary of An Adaptive Approach to Bayesian Optimization with Switching Costs, by Stefan Pricopie et al.
An adaptive approach to Bayesian Optimization with switching costs
by Stefan Pricopie, Richard Allmendinger, Manuel Lopez-Ibanez, Clyde Fare, Matt Benatan, Joshua Knowles
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 research investigates modifications to Bayesian Optimization for sequential experimental design in a resource-constrained setting, where changes to certain design variables incur a switching cost. The goal is to balance evaluating more and maintaining the same setup against switching and restricting the number of possible evaluations due to the incurred cost. Two process-constrained batch algorithms are adapted to this sequential problem formulation, along with two new methods: one cost-aware and one cost-ignorant. The proposed cost-aware hyperparameter-free algorithm yields comparable results to tuned process-constrained algorithms in all settings, suggesting some degree of robustness to varying landscape features and cost trade-offs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research explores how to make Bayesian Optimization work better when there are costs involved in changing certain things about the way you’re doing experiments. It’s like trying to balance two different goals: getting more information by doing more experiments, versus switching to a new approach that might be faster but gives less info. The researchers came up with two new ways of doing this, one that takes into account the cost of switching and one that doesn’t. They tested these methods on a bunch of math problems and found that the one that accounted for the cost worked pretty well in most cases. |
Keywords
» Artificial intelligence » Hyperparameter » Optimization
