Summary of Data-driven Preference Sampling For Pareto Front Learning, by Rongguang Ye et al.
Data-Driven Preference Sampling for Pareto Front Learning
by Rongguang Ye, Lei Chen, Weiduo Liao, Jinyuan Zhang, Hisao Ishibuchi
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 data-driven preference vector sampling framework for Pareto front learning is a technique that introduces preference vectors in a neural network to approximate complex Pareto fronts. Unlike previous methods, this approach efficiently samples preference vectors and accurately estimates the Pareto front by utilizing posterior information of objective functions to adjust the parameters of the sampling distribution flexibly. The method also designs the distribution of the preference vector as a mixture of Dirichlet distributions to improve performance in disconnected Pareto fronts. Extensive experiments validate the superiority of the proposed method compared with state-of-the-art algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to find the best solution that balances multiple competing goals, like maximizing profit while minimizing cost or environmental impact. This is called Pareto front learning. The challenge is to choose the right combinations of these goals in a way that makes sense for different situations. To solve this problem, the researchers designed a system that can adapt to changing conditions and find the best balance between the competing goals. They tested their method on various problems and showed it outperforms existing methods. |
Keywords
* Artificial intelligence * Neural network
