Summary of A Generalization Result For Convergence in Learning-to-optimize, by Michael Sucker and Peter Ochs
A Generalization Result for Convergence in Learning-to-Optimize
by Michael Sucker, Peter Ochs
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Probability (math.PR)
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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 A novel probabilistic framework for learning-to-optimize is proposed, which generalizes traditional deterministic optimization methods and establishes the convergence of learned optimization algorithms to stationary points with high probability. This framework can be seen as a statistical counterpart to geometric safeguards, ensuring convergence in non-smooth and non-convex loss functions. The main theorem demonstrates the effectiveness of this approach for parametric classes of potentially non-smooth and non-convex loss functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning algorithms are being developed to optimize certain problems. Usually, we show that these algorithms will get close to the right answer by using geometric arguments. However, these geometric arguments don’t easily apply to learned algorithms. To solve this problem, a new probabilistic framework is created. This framework helps prove that learned optimization algorithms can find good solutions most of the time. |
Keywords
» Artificial intelligence » Optimization » Probability
