Summary of Towards Stability Of Parameter-free Optimization, by Yijiang Pang et al.
Towards Stability of Parameter-free Optimization
by Yijiang Pang, Shuyang Yu, Bao Hoang, Jiayu Zhou
First submitted to arxiv on: 7 May 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 Hyperparameter tuning remains a significant challenge in adaptive gradient training methods, particularly when selecting an appropriate learning rate. This paper proposes a novel parameter-free optimizer, AdamG (Adam with the golden step size), designed to automatically adapt to diverse optimization problems without manual tuning. The core technique underlying AdamG is our golden step size derived for AdaGrad-Norm algorithm, which aims to preserve the tuning-free convergence and approximate the optimal step size in expectation w.r.t. various optimization scenarios. To evaluate tuning-free performance, we propose a novel evaluation criterion, reliability, in addition to classical performance criteria. Empirical results show that AdamG achieves superior performance compared to other parameter-free baselines, consistently on par with Adam using a manually tuned learning rate across various optimization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to find the perfect combination of ingredients for a recipe without knowing what works best. That’s kind of like what happens when training machines to learn from data. This paper proposes a new way to do this called AdamG, which automatically adjusts its settings based on the problem it’s trying to solve. By using a special formula, AdamG tries to find the best combination for each specific task. To test how well this works, we came up with a new way to measure success. Our results show that AdamG is better at finding good combinations than other methods, and performs similarly to when someone manually sets the right ingredients. |
Keywords
» Artificial intelligence » Hyperparameter » Optimization
