Summary of Offline Model-based Optimization by Learning to Rank, By Rong-xi Tan et al.
Offline Model-Based Optimization by Learning to Rank
by Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, Yao Wang, Yaoyuan Wang, Sheng Fu, Chao Qian
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 paper presents a new approach to offline model-based optimization (MBO) for identifying optimal designs given a fixed dataset and black-box function scores. The authors argue that traditional regression-based surrogate models, which prioritize precise predictions over score relationships, may not be well-suited for MBO due to the risk of out-of-distribution errors. They propose learning a ranking-based model using learning-to-rank techniques to prioritize promising designs based on their relative scores. This approach leverages order-maintaining quality rather than precise predictions, which leads to better generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline model-based optimization (MBO) is used to find the best design for a black-box function. The paper shows that traditional methods don’t work well because they try to predict scores precisely instead of ordering designs correctly. A new way to learn a ranking-based model is proposed, which does a better job at picking the best design. |
Keywords
» Artificial intelligence » Generalization » Optimization » Regression
