Summary of Offline Stochastic Optimization Of Black-box Objective Functions, by Juncheng Dong et al.
Offline Stochastic Optimization of Black-Box Objective Functions
by Juncheng Dong, Zihao Wu, Hamid Jafarkhani, Ali Pezeshki, Vahid Tarokh
First submitted to arxiv on: 3 Dec 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 paper introduces Stochastic Offline Black-Box Optimization (SOBBO) to tackle complex optimization problems in science and engineering. Existing offline black-box optimization methods fall short when dealing with stochasticity, so SOBBO addresses this by proposing two solutions: using a differentiable surrogate for large-data regimes or estimating gradients under conservative field constraints for scarce-data regimes. These approaches improve robustness, convergence, and data efficiency. The effectiveness of SOBBO is demonstrated through numerical experiments on both synthetic and real-world tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve big problems in science and engineering by finding the best answers quickly and accurately. It uses old data to figure out how to optimize complex functions without wasting time or resources. The method, called Stochastic Offline Black-Box Optimization (SOBBO), is better than others because it can handle uncertainty and surprises that happen in real life. |
Keywords
» Artificial intelligence » Optimization
