Summary of Enhancing Gaussian Process Surrogates For Optimization and Posterior Approximation Via Random Exploration, by Hwanwoo Kim and Daniel Sanz-alonso
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
by Hwanwoo Kim, Daniel Sanz-Alonso
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA); 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 This paper proposes novel noise-free Bayesian optimization strategies that leverage random exploration to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain ease of implementation like GP-UCB, but the added random step accelerates convergence nearly achieving optimal rates. Additionally, the authors propose using optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for unnormalized log-posterior density. For Bayesian inference with intractable likelihoods, they provide bounds for Hellinger distance between true and approximate posterior distributions in terms of design points. The effectiveness is demonstrated in non-convex benchmark objective functions, machine learning hyperparameter tuning, and black-box engineering design problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at finding the best settings by using a new way to mix exploration and exploitation in Bayesian optimization. It shows that this approach can find good solutions quickly and accurately, even when the problem is hard to solve. This could be useful for things like tuning machine learning models or designing engineering systems. |
Keywords
* Artificial intelligence * Bayesian inference * Hyperparameter * Machine learning * Optimization
