Summary of Sharpness-aware Black-box Optimization, by Feiyang Ye et al.
Sharpness-Aware Black-Box Optimization
by Feiyang Ye, Yueming Lyu, Xuehao Wang, Masashi Sugiyama, Yu Zhang, Ivor Tsang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel Sharpness-Aware Black-box Optimization (SABO) algorithm to address suboptimal model quality and generalization performance issues in existing black-box optimization methods. The SABO algorithm applies a sharpness-aware minimization strategy, reparameterizing the objective function by its expectation over a Gaussian distribution. This approach iteratively updates the parameterized distribution using approximated stochastic gradients of the maximum objective value within a small neighborhood around the current solution. Theoretical convergence rate and generalization bound analysis are provided, alongside empirical evidence demonstrating the effectiveness of SABO in improving model generalization performance on black-box prompt fine-tuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new way to help machines learn better. Right now, we use “black-box” methods that just try to make the machine learn really fast, but this can actually hurt how well it learns and performs later on. To fix this, the authors created an algorithm called SABO (Sharpness-Aware Black-box Optimization). SABO is like a smart way of updating what we know about the machine’s learning process. It uses math to make sure the machine doesn’t get stuck in bad places and improves its performance over time. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Objective function » Optimization » Prompt
