Summary of Design Editing For Offline Model-based Optimization, by Ye Yuan et al.
Design Editing for Offline Model-based Optimization
by Ye Yuan, Youyuan Zhang, Can Chen, Haolun Wu, Zixuan Li, Jianmo Li, James J. Clark, Xue Liu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 A novel approach to offline model-based optimization (MBO) is introduced in this paper, addressing the challenge of overly optimized designs in surrogate-model-based MBO. The proposed method, Design Editing for Offline Model-based Optimization (DEMO), leverages a diffusion prior to calibrate these designs. DEMO generates pseudo design candidates through gradient ascent with respect to a surrogate model and refines them using an editing process that injects noise and denoises with a trained diffusion prior. This ensures the final optimized designs align with the distribution of valid designs in the offline dataset. Theoretical analysis demonstrates that the difference between these designs and the prior distribution is controlled by the injected noise. Empirical evaluations on seven offline MBO tasks show DEMO outperforms baseline methods, achieving a mean rank of 2.1 and median rank of 1. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline model-based optimization (MBO) tries to find the best design without looking at new data. This is useful in areas like robotics, material science, and biotechnology. A common way to do this is by training a fake model using old designs and scores, then generating new designs by updating the fake model. However, this method can be bad because it might not work well on new, unseen designs. To solve this problem, researchers introduce a new approach called Design Editing for Offline Model-based Optimization (DEMO). DEMO generates fake design candidates by updating the fake model and refines these candidates by adding noise and then cleaning it up with a trained prior distribution. This makes sure the final designs are similar to valid designs in the offline dataset. The results show that DEMO is better than other methods, which is important for real-world applications. |
Keywords
» Artificial intelligence » Diffusion » Optimization
