Summary of Learning-rate-free Stochastic Optimization Over Riemannian Manifolds, by Daniel Dodd et al.
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
by Daniel Dodd, Louis Sharrock, Christopher Nemeth
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper addresses a significant challenge in gradient-based optimization over Riemannian manifolds by introducing innovative learning-rate-free algorithms for stochastic optimization. The proposed methods eliminate the need for meticulous hyperparameter tuning and provide a more robust and user-friendly approach. The authors establish high probability convergence guarantees that are optimal, up to logarithmic factors, compared to the best-known optimally tuned rate in the deterministic setting. Numerical experiments demonstrate competitive performance against learning-rate-dependent algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to optimize things on curved spaces called Riemannian manifolds. Right now, people need to do a lot of trial and error to get the right settings for their calculations to work well. The researchers have come up with new ways to do this that don’t require as much fiddling around. They’ve also shown that these new methods can be trusted to give good results most of the time. |
Keywords
» Artificial intelligence » Hyperparameter » Optimization » Probability
