Summary of Generalization Bounds For Mixing Processes Via Delayed Online-to-pac Conversions, by Baptiste Abeles et al.
Generalization bounds for mixing processes via delayed online-to-PAC conversions
by Baptiste Abeles, Eugenio Clerico, Gergely Neu
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes an analytical framework for evaluating the generalization error of statistical learning algorithms in non-i.i.d. data scenarios, where the training data is sampled from a stationary mixing process. The authors develop a reduction to online learning with delayed feedback and show that the existence of an algorithm with bounded regret against a fixed statistical learning method implies low generalization error even when the data sequence is sampled from a time series with dependence between consecutive points. The results demonstrate a trade-off between delay in the online game and degree of dependence, achieving near-optimal rates in well-studied settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well machine learning models work on new, unseen data if they were trained on data that wasn’t randomly selected. It finds a way to relate this problem to another problem called online learning with delayed feedback. The authors show that having an algorithm that does well in this game of online learning means the model will also do well on new data. The results depend on how much delay is allowed in the game and how dependent the data points are. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Online learning » Time series
