Summary of Bayesian Optimization Of Functions Over Node Subsets in Graphs, by Huidong Liang et al.
Bayesian Optimization of Functions over Node Subsets in Graphs
by Huidong Liang, Xingchen Wan, Xiaowen Dong
First submitted to arxiv on: 24 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 novel framework for combinatorial optimization on graphs utilizes Bayesian Optimization (BO), a sample-efficient black-box solver, to optimize over functions defined on node subsets. The framework maps each k-node subset to a node in a new combinatorial graph and adopts a local modeling approach to efficiently traverse the latter graph by progressively sampling its subgraphs using a recursive algorithm. Extensive experiments demonstrate the effectiveness of the proposed BO framework on various types of graphs and optimization tasks, including ablation studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a special kind of computer programming called Bayesian Optimization (BO) to help solve problems that involve searching through many possible solutions in a graph. Graphs are like maps with lines and nodes, and this algorithm is good at finding the best solution by looking at small parts of the map first. It works well on different types of graphs and helps find the right answer quickly. |
Keywords
» Artificial intelligence » Optimization
