Summary of Near Optimal Pure Exploration in Logistic Bandits, by Eduardo Ochoa Rivera et al.
Near Optimal Pure Exploration in Logistic Bandits
by Eduardo Ochoa Rivera, Ambuj Tewari
First submitted to arxiv on: 28 Oct 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 This paper bridges the gap in optimal algorithms for pure exploration problems in generalized linear model (GLM) bandits. Specifically, it develops the first track-and-stop algorithm, called logistic track-and-stop (Log-TS), which efficiently solves general pure exploration problems under the logistic bandit setting. Log-TS achieves an approximation of the instance-specific lower bound of the expected sample complexity up to a logarithmic factor. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can make good decisions when they don’t know what’s going to happen next. It creates a new way for computers to explore and learn from experiences, which is really useful in real-world situations. The new method, called logistic track-and-stop (Log-TS), is very efficient and does a great job of finding the best solution. |
