Summary of Bayesian Off-policy Evaluation and Learning For Large Action Spaces, by Imad Aouali et al.
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
by Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 introduces a unified Bayesian framework for off-policy evaluation (OPE) and learning (OPL) in large action spaces. The framework captures correlated actions through structured priors, enabling more sample-efficient OPE and OPL. A generic approach called sDM is proposed, which leverages action correlations without compromising computational efficiency. Additionally, the paper introduces Bayesian metrics that assess average performance across multiple problem instances, deviating from worst-case assessments. Empirical evidence demonstrates the strong performance of sDM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to learn and evaluate actions in systems where actions are related. The researchers developed a new way to use Bayesian methods for off-policy evaluation and learning, which is more efficient when there are many possible actions. They also came up with a new metric that looks at how well algorithms perform on average, rather than just the worst-case scenario. This could be useful in many applications where we want to make decisions based on past experiences. |
