Summary of Nonmyopic Global Optimisation Via Approximate Dynamic Programming, by Filippo Airaldi et al.
Nonmyopic Global Optimisation via Approximate Dynamic Programming
by Filippo Airaldi, Bart De Schutter, Azita Dabiri
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces novel nonmyopic acquisition strategies for unconstrained global optimization, specifically designed for inverse distance weighting (IDW) and radial basis functions (RBF) based methods. These strategies, which optimize a sequence of query points over a horizon, enable lookahead acquisition by predicting the evolution of the surrogate model. The proposed approach extends nonmyopic acquisition principles from Bayesian optimization to deterministic frameworks, demonstrating improved performance on synthetic and hyperparameter tuning benchmark problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the best solution for a difficult problem without knowing how good each potential solution is. It’s like trying to find the highest mountain by only seeing a small piece of the landscape at a time. The researchers are trying to make this process better by looking ahead and planning what to do next, instead of just focusing on the immediate best option. They’re testing new ways of doing this that work well with different types of problems. |
Keywords
» Artificial intelligence » Hyperparameter » Optimization
