Summary of Smoothed Robust Phase Retrieval, by Zhong Zheng and Lingzhou Xue
Smoothed Robust Phase Retrieval
by Zhong Zheng, Lingzhou Xue
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP); Statistics Theory (math.ST); Methodology (stat.ME)
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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 The paper proposes a new approach to phase retrieval in noisy environments, introducing the smoothed robust phase retrieval (SRPR) method based on convolution-type smoothed loss functions. The SRPR addresses the limitations of traditional methods by enjoying a benign geometric structure, with no spurious local solutions under noiseless conditions and a benign landscape even when corrupted. This is achieved through a combination of theoretical analysis and empirical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a tricky problem called phase retrieval, where you try to get back the original signal from some noisy measurements. They made it smoother so that it’s easier to optimize, and they proved some good things about how this new method works. It means we can recover signals even with some noise in them! |
