Summary of Offline Multi-objective Optimization, by Ke Xue et al.
Offline Multi-Objective Optimization
by Ke Xue, Rong-Xi Tan, Xiaobin Huang, Chao Qian
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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 proposed benchmark for offline multi-objective optimization (MOO) aims to bridge the gap between offline single-objective optimization (SOO) and MOO. The benchmark provides a range of problems, datasets, and open-source examples for method comparisons and advancements in offline MOO. The authors analyze how current related methods can be adapted to offline MOO from four fundamental perspectives: data, model architecture, learning algorithm, and search algorithm. Empirical results show improvements over the best value of the training set, demonstrating the effectiveness of offline MOO methods. Despite no particular method standing out significantly, there is still an open challenge in further enhancing the effectiveness of offline MOO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. The authors propose a benchmark for offline multi-objective optimization (MOO), which provides tasks, datasets, and open-source examples. This benchmark can be used as a foundation for method comparisons and advancements in offline MOO. |
Keywords
» Artificial intelligence » Objective function » Optimization
