Summary of Asymptotic Time-uniform Inference For Parameters in Averaged Stochastic Approximation, by Chuhan Xie et al.
Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
by Chuhan Xie, Kaicheng Jin, Jiadong Liang, Zhihua Zhang
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates statistical inference for parameters in stochastic approximation (SA), a broad field encompassing optimization and machine learning applications. The authors analyze the convergence rates of averaged iterates in linear and nonlinear SA problems, leading to the development of three types of asymptotic confidence sequences with uniform coverage guarantees across all times. These guarantees remain valid when replacing the unknown covariance matrix with its plug-in estimator. Experiments validate the proposed methodology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make accurate predictions about things that change over time in machine learning and optimization problems. The researchers figure out how quickly averages of random numbers get close to a certain target, then they create special tools to estimate where this target is with confidence. They test their ideas and show they work well. |
Keywords
» Artificial intelligence » Inference » Machine learning » Optimization
