Summary of Deep Learning From Strongly Mixing Observations: Sparse-penalized Regularization and Minimax Optimality, by William Kengne and Modou Wade
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
by William Kengne, Modou Wade
First submitted to arxiv on: 12 Jun 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 The paper explores the optimization and regularization of deep neural networks for dependent data, focusing on strongly mixing observations. It develops a general framework that includes regression estimation, classification, time series prediction, among others. The study establishes oracle inequalities for expected excess risk and provides bounds on class of Hölder smooth functions. The results also investigate nonparametric regression from strong mixing data, achieving sub-exponential error rates. Additionally, the paper examines nonparametric autoregression with Gaussian and Laplace errors, establishing a minimax optimal rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning from dependent data is crucial for many applications. This paper helps bridge the gap by developing methods to regularize deep neural networks when working with strongly mixing observations. It shows how to achieve good results in tasks like regression estimation, classification, and time series prediction. The findings provide a framework that can be used in various contexts. |
Keywords
» Artificial intelligence » Classification » Deep learning » Optimization » Regression » Regularization » Time series
