Summary of Unified Pac-bayesian Study Of Pessimism For Offline Policy Learning with Regularized Importance Sampling, by Imad Aouali et al.
Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling
by Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba
First submitted to arxiv on: 5 Jun 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 proposes a comprehensive framework for analyzing pessimism in off-policy learning (OPL), a method that corrects bias from logging policies used to collect data. The authors introduce a PAC-Bayesian framework that universally applies to various importance weight regularizations, enabling their comparison within a single framework. They derive a tractable generalization bound and provide empirical results that challenge common understanding, demonstrating the effectiveness of standard IW regularization techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-policy learning helps machines learn from data collected by other policies. But this method can be biased if it uses data collected under different rules. To fix this problem, researchers use importance weighting to correct for bias. However, this method can also produce results that are not reliable or consistent. To address this issue, the authors introduce a new framework that helps analyze and compare different methods for correcting bias. This framework is based on PAC-Bayesian theory, which provides a mathematical way to understand how well a model will generalize to unseen data. |
Keywords
» Artificial intelligence » Generalization » Regularization
