Summary of Online Estimation Via Offline Estimation: An Information-theoretic Framework, by Dylan J. Foster et al.
Online Estimation via Offline Estimation: An Information-Theoretic Framework
by Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 new framework, Oracle-Efficient Online Estimation (OEOE), enables the conversion of offline estimation algorithms into online estimation algorithms in a black-box fashion. This paper introduces OEOE, which allows learners to interact with data streams indirectly through a sequence of offline estimators produced by a black-box algorithm operating on the stream. The authors investigate the statistical and computational complexity of online estimation within this framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to estimate something based on data that’s being generated in real-time. This paper is about finding a way to take algorithms designed for estimating things from fixed datasets and use them for estimating things as the data comes in. They call it “online estimation” and it’s important because it lets people make decisions or predictions while they’re still getting more information. |
