Summary of Finite Sample and Large Deviations Analysis Of Stochastic Gradient Algorithm with Correlated Noise, by George Yin and Vikram Krishnamurthy
Finite Sample and Large Deviations Analysis of Stochastic Gradient Algorithm with Correlated Noise
by George Yin, Vikram Krishnamurthy
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 investigates the performance of a stochastic gradient algorithm with decreasing step sizes in finite sample settings, considering correlated noise. The researchers employ a perturbed Lyapunov function to develop a systematic approach for analyzing the algorithm’s regret. Furthermore, they analyze the time it takes for the iterates to escape using large deviations theory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well a special kind of computer algorithm performs when dealing with noisy data and limited samples. The researchers found a new way to understand how this algorithm works by using a mathematical trick called a perturbed Lyapunov function. They also studied how long it takes for the algorithm’s calculations to get stuck in a certain state. |
