Summary of Logarithmic Smoothing For Pessimistic Off-policy Evaluation, Selection and Learning, by Otmane Sakhi and Imad Aouali and Pierre Alquier and Nicolas Chopin
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
by Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin
First submitted to arxiv on: 23 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 This paper tackles the offline contextual bandit problem, where historical interactions are used to evaluate, select, and learn better policies. The authors move beyond point estimators, adopting a pessimistic approach that constructs upper bounds for assessing policy performance, ensuring confident selection and learning. They introduce novel concentration bounds for importance weighting risk estimators, covering existing methods and paving the way for new ones. Specifically, they develop a logarithmically smoothed estimator (LS), which outperforms competitors in policy evaluation, selection, and learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn from past experiences to choose better options. Imagine you want to try a different route to work or recommend a new movie to friends. The authors use math to make sure we can trust the choices we make based on what happened before. They created special formulas to help us pick the best option, even when things are uncertain. This is important for many real-life applications, like choosing the best medicine or optimizing traffic flow. |
