Summary of Comparisons Are All You Need For Optimizing Smooth Functions, by Chenyi Zhang and Tongyang Li
Comparisons Are All You Need for Optimizing Smooth Functions
by Chenyi Zhang, Tongyang Li
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC)
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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 When optimizing machine learning models, gradient computations can be challenging or even infeasible. To address this issue, the paper systematically studies optimization of a smooth function only assuming an oracle that compares function values at two points and tells which is larger. The authors provide algorithms for convex and nonconvex functions, showing that comparisons are sufficient for optimizing smooth functions using derivative-free methods. This approach has applications in reinforcement learning with human feedback in large language models. The paper’s results match the best-known zeroth-order algorithms, suggesting that comparisons can be used instead of function evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When optimizing machine learning models, there are various scenarios where gradient computations are challenging or even infeasible. In this paper, researchers study optimization of a smooth function using only an oracle that compares function values at two points and tells which is larger. The results show that comparisons can be used instead of function evaluations to optimize smooth functions. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Reinforcement learning
