Summary of Optimizing Posterior Samples For Bayesian Optimization Via Rootfinding, by Taiwo A. Adebiyi and Bach Do and Ruda Zhang
Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
by Taiwo A. Adebiyi, Bach Do, Ruda Zhang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 Bayesian optimization is a powerful tool for optimizing complex objective functions. This paper introduces an efficient global optimization strategy that leverages Bayesian optimization to optimize the acquisition function, which in turn optimizes the costly objective function. The proposed approach uses global rootfinding to select judiciously two sets of starting points that balance exploration and exploitation. Surprisingly, even with a single point from each set, the algorithm can discover the global optimum most of the time. Moreover, the algorithm scales linearly to high dimensions, breaking the curse of dimensionality. The paper demonstrates remarkable improvement in Bayesian optimization using Gaussian process Thompson sampling (GP-TS) and other posterior sample-based acquisition functions. A sample-average formulation is also proposed, which allows for explicit control of exploitation. Our implementation is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian optimization helps find the best option among many possibilities. This paper makes it better by introducing a new way to optimize something called an “acquisition function”. The trick is to use two sets of starting points that balance searching and trying out options. Surprisingly, even with just one point from each set, this method can often find the best solution. What’s more, it works well even when dealing with many variables at once. The paper shows how this approach improves a popular method called Gaussian process Thompson sampling (GP-TS) and also proposes a new way to control how much the algorithm tries out options. |
Keywords
» Artificial intelligence » Objective function » Optimization
