Summary of Stochastic Gradient Descent with Adaptive Data, by Ethan Che and Jing Dong and Xin T. Tong
Stochastic Gradient Descent with Adaptive Data
by Ethan Che, Jing Dong, Xin T. Tong
First submitted to arxiv on: 2 Oct 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 A novel approach to addressing the challenges posed by non-stationary data in policy optimization problems is proposed in this paper. The stochastic gradient descent (SGD) algorithm, widely used in online learning scenarios, is adapted for policy optimization in operations research. Unlike traditional iid datasets, the adaptively generated data stream in policy optimization problems exhibits correlations between samples, introducing bias in the gradient estimate and potential instability. To mitigate these issues, the authors introduce simple criteria for ensuring the convergence of SGD, leveraging Lyapunov-function analysis to translate existing stability results from operations research into convergence rates. The proposed approach is demonstrated on queueing and inventory management problems, highlighting its applicability to studying sample complexity in actor-critic policy gradient algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning! When we try to optimize policies in real-world situations, the data changes because our decisions affect what happens next. This makes it hard for our algorithm to learn and improve. The authors of this paper figured out how to make an important optimization technique called stochastic gradient descent work even when the data isn’t independent and identically distributed (a fancy way of saying “random”). They did this by showing that if we take into account how fast the policy changes the environment, we can guarantee that the algorithm will converge. This is a big deal because it means we can now study more complex problems in fields like operations research. |
Keywords
» Artificial intelligence » Machine learning » Online learning » Optimization » Stochastic gradient descent
