Summary of Adaptive Debiased Sgd in High-dimensional Glms with Streaming Data, by Ruijian Han et al.
Adaptive debiased SGD in high-dimensional GLMs with streaming data
by Ruijian Han, Lan Luo, Yuanhang Luo, Yuanyuan Lin, Jian Huang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 online inference in high-dimensional generalized linear models is introduced, which enables real-time analysis of sequentially collected data without requiring full dataset access or large-dimensional summary statistics storage. The method uses an adaptive stochastic gradient descent algorithm and a novel online debiasing procedure to maintain low-dimensional summary statistics while controlling the optimization error introduced by dynamically changing loss functions. The proposed Adaptive Debiased Lasso (ADL) estimator is shown to be asymptotically normal, with statistical validity and computational efficiency demonstrated through simulation experiments and a real data application to spam email classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to analyze data that’s being collected in real-time, without needing all the data at once. It uses a special kind of algorithm that can adapt to changing data and keep track of important information without storing too much data. This approach is tested on simulations and a real-world example, showing it works well for classifying spam emails. |
Keywords
» Artificial intelligence » Classification » Inference » Optimization » Stochastic gradient descent
